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Статті в журналах з теми "Detection of road surface conditions":
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Choi, Wansik, Jun Heo, and Changsun Ahn. "Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset." Sensors 21, no. 22 (November 22, 2021): 7769. http://dx.doi.org/10.3390/s21227769.
Анотація:
Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.
2
Piccardi, Armando, and Lorenzo Colace. "Optical Detection of Dangerous Road Conditions." Sensors 19, no. 6 (March 19, 2019): 1360. http://dx.doi.org/10.3390/s19061360.
Анотація:
We demonstrated an optical method to evaluate the state of asphalt due to the presence of atmospheric agents using the measurement of the polarization/depolarization state of near infrared radiation. Different sensing geometries were studied to determine the most efficient ones in terms of performance, reliability and compactness. Our results showed that we could distinguish between a safe surface and three different dangerous surfaces, demonstrating the reliability and selectivity of the proposed approach and its suitability for implementing a sensor.
3
Lee, Taehee, Yeohwan Yoon, Chanjun Chun, and Seungki Ryu. "CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes." Electronics 10, no. 12 (June 10, 2021): 1402. http://dx.doi.org/10.3390/electronics10121402.
Анотація:
Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.
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Songsong Xu, Songsong Xu, Chi Ruan Chi Ruan, and Lili Feng Lili Feng. "Road surface condition sensor based on scanning detection of backward power." Chinese Optics Letters 12, no. 5 (2014): 050801–50804. http://dx.doi.org/10.3788/col201412.050801.
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Bouilloud, L., E. Martin, F. Habets, A. Boone, P. Le Moigne, J. Livet, M. Marchetti, et al. "Road Surface Condition Forecasting in France." Journal of Applied Meteorology and Climatology 48, no. 12 (December 1, 2009): 2513–27. http://dx.doi.org/10.1175/2009jamc1900.1.
Анотація:
Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.
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Kumari Dara, Anitha, and Dr A. Govardhan. "Detection of Coordinate Based Accident-Prone Areas on Road Surface using Machine Learning Methods." International Journal of Computer Engineering and Information Technology 12, no. 3 (March 31, 2020): 19–25. http://dx.doi.org/10.47277/ijceit/12(3)1.
Анотація:
The growth in the road networks in India and other developing countries have influenced the growth in transport industry and other industries, which depends on the road network for operations. The industries such as postal services or mover services have influenced the similar growths in these industries as well. However, the dependency of these industries is high on the road surface conditions and any deviation on the road surface conditions can also influence the performance of the services provided by the mentioned services. Nonetheless, the conditions of the road surface are one of the prime factors for road safety and number of evidences are found, which are discussed in subsequent sections of this work, that the bad road surface conditions are increasing the road accidents. Several parallel research attempts are deployed in order to find out, the regions where the road surface conditions are not proper, and the traffic density is higher. Nevertheless, outcomes of these parallel works are highly criticised due to the lack of accuracy in detection of the road surface defects, detection of accurate location of the defects and detection of the traffic density data from various sources. Thus, this work proposes a novel framework for detection of the road defect and further mapping to the spatial data coordinates resulting into the detection of the accident-prone zones or accident affinities of the roads. This work deploys a self-adjusting parametric coefficient-based regression model for detection of the risk factors of the road defects and in the other hand, extracts the traffic density of the road regions and further maps the accident affinities. This work outcomes into 97.69% accurate detection of the road accident affinity and demonstrates less complexity compared with the other parallel research outcomes
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Kumar, P., and E. Angelats. "AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 91–96. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-91-2017.
Анотація:
Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.
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Dong, Dapeng, and Zili Li. "Smartphone Sensing of Road Surface Condition and Defect Detection." Sensors 21, no. 16 (August 12, 2021): 5433. http://dx.doi.org/10.3390/s21165433.
Анотація:
Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.
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Takeuchi, Kazuya, Keiji Shibata, and Yuukou Horita. "Detection of Road Surface Conditions in Winter using CCTV Camera for Road Monitoring." IEEJ Transactions on Electronics, Information and Systems 135, no. 7 (2015): 901–7. http://dx.doi.org/10.1541/ieejeiss.135.901.
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Sharma, Sunil Kumar, Haidang Phan, and Jaesun Lee. "An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors." Applied Sciences 10, no. 13 (June 29, 2020): 4490. http://dx.doi.org/10.3390/app10134490.
Анотація:
Road surface monitoring is an essential problem in providing smooth road infrastructure to commuters. This paper proposed an efficient road surface monitoring using an ultrasonic sensor and image processing technique. A novel cost-effective system, which includes ultrasonic sensors sensing with GPS for the detection of the road surface conditions, was designed and proposed. Dynamic time warping (DTW) technique was incorporated with ultrasonic sensors to improve the classification and accuracy of road surface detecting conditions. A new algorithm, HANUMAN, was proposed for automatic recognition and calculation of pothole and speed bumps. Manual inspection was performed and comparison was undertaken to validate the results. The proposed system showed better efficiency than the previous systems with a 95.50% detection rate for various road surface irregularities. The novel framework will not only identify the road irregularities, but also help in decreasing the number of accidents by alerting drivers.
Дисертації з теми "Detection of road surface conditions":
1
Hu, Yazhe. "Degenerate Near-planar Road Surface 3D Reconstruction and Automatic Defects Detection." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98671.
Анотація:
This dissertation presents an approach to reconstruct degenerate near-planar road surface in three-dimensional (3D) while automatically detect road defects. Three techniques are developed in this dissertation to establish the proposed approach. The first technique is proposed to reconstruct the degenerate near-planar road surface into 3D from one camera. Unlike the traditional Structure from Motion (SfM) technique which has the degeneracy issue for near-planar object 3D reconstruction, the uniqueness of the proposed technique lies in the use of near-planar characteristics of surfaces in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem using only two images. Following the accuracy-enhanced 3D reconstructed road surface, the second technique automatically detects and estimates road surface defects. As the 3D surface is inversely solved from 2D road images, the detection is achieved by jointly identifying irregularities from the 3D road surfaces and the corresponding image information, while clustering road defects and obstacles using a mean-shift algorithm with flat kernel to estimate the depth, size, and location of the defects. To enhance the physics-driven automatic detection reliability, the third technique proposes and incorporates a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from supervised learning approaches which need labeled training images, the road anomaly detection network is trained by road surface images that are automatically labeled based on the reconstructed 3D surface information. In order to collect clear road surface images on the public road, a road surface monitoring system is designed and integrated for the road surface image capturing and visualization. The proposed approach is evaluated in both simulated environment and through real-world experiments. The parametric study of the proposed approach shows the small error of the 3D road surface reconstruction influenced by different variables such as the image noise, camera orientation, and the vertical movement of the camera in a controlled simulation environment. The comparison with traditional SfM technique and the numerical results of the proposed reconstruction using real-world road surface images then indicate that the proposed approach effectively reconstructs high quality near-planar road surface while automatically detects road defects with high precision, accuracy, and recall rates without the degenerate issue.Doctor of Philosophy
Road is one of the key infrastructures for ground transportation. A good road surface condition can benefit mainly on three aspects: 1. Avoiding the potential traffic accident caused by road surface defects, such as potholes. 2. Reducing the damage to the vehicle initiated by the bad road surface condition. 3. Improving the driving and riding comfort on a healthy road surface. With all the benefits mentioned above, it is important to examine and check the road surface quality frequently and efficiently to make sure that the road surface is in a healthy condition. In order to detect any road surface defects on public road in time, this dissertation proposes three techniques to tackle the road surface defects detection problem: First, a near-planar road surface three-dimensional (3D) reconstruction technique is proposed. Unlike traditional 3D reconstruction technique, the proposed technique solves the degenerate issue for road surface 3D reconstruction from two images. The degenerate issue appears when the object reconstructed has near-planar surfaces. Second, after getting the accuracy-enhanced 3D road surface reconstruction, this dissertation proposes an automatic defects detection technique using both the 3D reconstructed road surface and the road surface image information. Although physics-based detection using 3D reconstruction and 2D images are reliable and explainable, it needs more time to process these data. To speed up the road surface defects detection task, the third contribution is a technique that proposes a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from traditional neural network-based detection techniques, the proposed combines the 3D road information with the CNN output to jointly determine the road surface defects region. All the proposed techniques are evaluated using both the simulation and real-world experiments. Results show the efficacy and efficiency of the proposed techniques in this dissertation.
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Lorentzon, Mattis, and Tobias Andersson. "Road Surface Modeling using Stereo Vision." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78455.
Анотація:
Modern day cars are often equipped with a variety of sensors that collect information about the car and its surroundings. The stereo camera is an example of a sensor that in addition to regular images also provides distances to points in its environment. This information can, for example, be used for detecting approaching obstacles and warn the driver if a collision is imminent or even automatically brake the vehicle. Objects that constitute a potential danger are usually located on the road in front of the vehicle which makes the road surface a suitable reference level from which to measure the object's heights. This Master's thesis describes how an estimate of the road surface can be found to in order to make these height measurements. The thesis describes how the large amount of data generated by the stereo camera can be scaled down to a more effective representation in the form of an elevation map. The report discusses a method for relating data from different instances in time using information from the vehicle's motion sensors and shows how this method can be used for temporal filtering of the elevation map. For estimating the road surface two different methods are compared, one that uses a RANSAC-approach to iterate for a good surface model fit and one that uses conditional random fields for modeling the probability of different parts of the elevation map to be part of the road. A way to detect curb lines and how to use them to improve the road surface estimate is shown. Both methods for road classification show good results with a few differences that are discussed towards the end of the report. An example of how the road surface estimate can be used to detect obstacles is also included.
3
Ye, Maosheng. "Road Surface Condition Detection and Identification and Vehicle Anti-Skid Control." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1227197539.
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Zhang, Hongyi. "Road surface condition detection for autonomous vehicle by NIR LED system and machine learning approaches." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST106.
Анотація:
Le domaine des véhicules autonomes a suscité un grand intérêt ces dernières années. Afin de garantir au passager une expérience sûre et confortable sur les véhicules autonomes, des systèmes d'obstacles avancés doivent être mis en œuvre. Bien que les solutions actuelles de détection d'obstacles aient montré de bonnes performances, elles doivent être encore améliorées pour une sécurité accrue des véhicules autonomes sur route, de jour comme de nuit. En particulier, les véhicules autonomes dans la vie réelle peuvent rencontrer de la glace, de la neige ou des flaques d'eau, qui peuvent être la cause de collisions graves et d'accidents de la circulation. Les systèmes de détection doivent donc permettre de détecter les changements d'état de la route pour anticiper la réaction du véhicule et/ou désactiver les fonctions automatisées. L'objectif de cette thèse est de proposer un système pour les véhicules autonomes afin de détecter les conditions de chaussée induites par la météo. Après une étude approfondie de l'état de l'art, un système proche infrarouge (NIR) basé sur des LED et un système d'apprentissage automatique sont proposés pour la détection diurne et nocturne. Le système NIR a été conçu puis validé expérimentalement et, les spécifications techniques du système ont été définies. Le système d'apprentissage automatique est de plus proposé comme solution complémentaire au système NIR. Différents modèles d'apprentissage ont été testés et comparés en termes de performance. Enfin, les résultats sont discutés et une combinaison des deux systèmes est proposée afin de garantir une performance accrue pour la reconnaissance des conditions de routeThe field of autonomous vehicles has aroused great interest in recent years. In order to ensure the passenger to get a safe and comfortable experience on autonomous vehicles, advanced obstacle systems have to be implemented. Although current solutions for detecting obstacles have shown quite good performances, they have to be improved for an increased safety of autonomous vehicles on road, both in day-time and night-time conditions. In particular, autonomous vehicles in real life may encounter ice, snow or water puddles, which may be the cause of severe crashes and traffic accidents. The detection systems must hence allow detecting changes in road conditions to anticipate the vehicle reaction and/or deactivate the automated functions. The aim of this thesis is to propose a system implemented on the autonomous vehicles in order to detect the road surface conditions induced by the weather. After deep investigation of the state of art, a near infrared (NIR) system based on LEDs and a machine learning system were proposed for daytime and night-time detection. The NIR systems with three LEDs were investigated with experimental validations. In addition, the specifications of the NIR systems are carefully discussed. Furthermore, the machine learning system is proposed as a supplementary system. The performance of different models is compared in terms of classification accuracy and model complexity. Finally, the results are discussed and a combination of the two systems is proposed
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Chen, Guangyu. "Texture Based Road Surface Detection." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1213805526.
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Abbas, Mohammad. "Remote sensing of road surface conditions." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7379/.
Анотація:
The remote real time identification of road surfaces is an increasingly important task in the automotive world. The development of automotive active safety system requires a remote sensing technology that alerts drivers to potential hazards such as slippery surfaces caused by water, mud, ice, snow etc. This will improve the safety of driving and reduce the road accidents all over the world. This thesis is dedicated to the experimental study of the feasibility of an affordable short-range ultrasonic and radar system for road surface recognition ahead of a vehicle. It introduces a developed novel system which can recognize the surfaces for all terrains (both on-road and off-road) based on the analysis of backscattered signals. Fundamental theoretical analysis, extensive modelling and practical experiments demonstrated that the use of pattern recognition techniques allows for reliable discrimination of the surfaces of interest. The overall classification system is described, including features extraction and their number reduction, as well as optimization of the algorithms. The performance of 4 classification algorithms was assessed and evaluated to confirm the effectiveness of the system. Several aspects like the complexity of the classification algorithms and the priori knowledge of the environment were investigated to explore the potential of this research and the possibility of introducing the surface classification system into the automotive market in the nearest future.
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Li, Yaqi. "Road Pothole Detection System Based on Stereo Vision." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1525708920748809.
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Clark, Robin Tristan. "The integration of cloud satellite images with prediction of icy conditions on Devon's roads." Thesis, University of Plymouth, 1997. http://hdl.handle.net/10026.1/1844.
Анотація:
The need for improved cloud parameterisations in a road surface temperature model is demonstrated. Case studies from early 1994 are used to investigate methods of tracking cloud cover using satellite imagery and upper level geostrophic flow. Two of these studies are included in this thesis. Errors encountered in cloud tracking methods were investigated as well as relationships between cloud height and pixel brightness in satellite imagery. For the first time, a one dimensional energy balance model is developed to investigate the effects of erroneous cloud forecasts on surface temperature. The model is used to determine detailed dependency of surface freezing onset time and minimum temperature on cloud cover. Case studies from the 1995/96 winter in Devon are undertaken to determine effects of differing scenarios of cloud cover change. From each study, an algorithm for predicting road surface temperature is constructed which could be used in future occurrences of the corresponding scenario of the case study. Emphasis is strongly placed on accuracy of predictions of surface freezing onset time and minimum surface temperature. The role o f surface and upper level geostrophic flow, humidity and surface wetness in temperature prediction is also investigated. In selected case studies, mesoscale data are also analysed and compared with observations to determine feasibility of using mesoscale models to predict air temperature. Finally, the algorithms constructed from the 1995/96 studies are tested using case studies from the 1996/97 winter. This winter was significantly different from its preceding one which consequently meant that the algorithm from only one scenario of the 1995/96 winter could be tested. An algorithm is also constructed from a 1996/97 winter case study involving a completely different scenario Recommendations for future research suggest testing of existing algorithms with guidance on additional scenarios.
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Wang, Ting. "Effect of surface conditions on DNA detection sensitivity by silicon based bio-sensing devices /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20WANGT.
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Sorosac, Nicole. "Etude d'un système d'inspection optique d'état de surface de bobines d'acier inoxydable laminées à froid." Grenoble 1, 1988. http://www.theses.fr/1988GRE10164.
Книги з теми "Detection of road surface conditions":
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Perchanok, M. S. Evaluation of a video system for remote monitoring of winter road surface conditions. Downsview, Ont: Research and Development Branch, Ministry of Transportation, 1994.
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Cushman, Samuel A., and Tzeidle N. Wasserman. Quantifying loss and degradation of former American marten habitat due to the impacts of forestry operations and associated road networks in northern Idaho, USA. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198759805.003.0012.
Анотація:
American marten are associated with extensive and unfragmented late seral forest habitats, and are often considered to be particularly vulnerable to habitat loss and fragmentation. This chapter evaluates the impact of road building and timber harvest on habitat suitability for marten in northern Idaho, USA, using an empirically derived, multi-scale habitat suitability model, reconstructing key predictor variables (elevation, forest type, road density, canopy cover, landscape fragmentation and the extensiveness of late seral forest in the landscape) as they appear to have existed prior to harvest, and applying the model to both current and pre-harvest conditions. Calculating changes in the extent and pattern of habitat in the landscape indicate that timber harvest and road construction together reduced marten habitat quality considerably across the study area, which is likely responsible for current patterns of reduced detection rates and lower genetic diversity in areas that have experienced the largest amounts of habitat loss.
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Johnson, Susan Lee. Writing Kit Carson. University of North Carolina Press, 2020. http://dx.doi.org/10.5149/northcarolina/9781469658834.001.0001.
Анотація:
This critical biography braids together lives over time and space, telling tales of two white women who, in the 1960s, wrote books about the fabled frontiersman Christopher "Kit" Carson: Quantrille McClung, a Denver librarian who compiled the Carson-Bent-Boggs Genealogy, and Kansas-born but Washington, D.C.- and Chicago-based Bernice Blackwelder, a singer on stage and radio, a CIA employee, and the author of Great Westerner: The Story of Kit Carson. In the 1970s, as once-celebrated figures like Carson were falling headlong from grace, these two amateur historians kept weaving stories of western white men, including those who married American Indian and Spanish Mexican women, just as Carson had wed Singing Grass, Making Out Road, and Josefa Jaramillo. This multilayered biography reveals the nature of relationships between women historians and male historical subjects and between history buffs and professional historians. It explores the practice of history in the context of everyday life, the seductions of gender in the context of racialized power, and the strange contours of twentieth-century relationships predicated on nineteenth-century pasts. On the surface, it tells a story of lives tangled across generation and geography. Underneath run probing questions about how we know about the past and how that knowledge is shaped by the conditions of our knowing.
Частини книг з теми "Detection of road surface conditions":
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Kutila, Matti, Maria Jokela, Bernd Roessler, and Jürgen Weingart. "Utilization of Optical Road Surface Condition Detection around Intersections." In Advanced Microsystems for Automotive Applications 2009, 109–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00745-3_9.
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Li, Qingquan, Yong Liu, and Qingzhou Mao. "Design and Applications of an Integrated Multi-Sensor Mobile System for Road Surface Condition Detection." In Geospatial Technology for Earth Observation, 45–61. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0050-0_3.
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Baltazart, V., J. M. Moliard, R. Amhaz, L. M. Cottineau, A. Wright, D. Wright, and M. Jethwa. "Automatic Crack Detection on Pavement Images for Monitoring Road Surface Conditions—Some Results from the Collaborative FP7 TRIMM Project." In RILEM Bookseries, 719–24. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-024-0867-6_100.
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Gavilán, M., D. Balcones, M. A. Sotelo, D. F. Llorca, O. Marcos, C. Fernández, I. García, and R. Quintero. "Surface Classification for Road Distress Detection System Enhancement." In Computer Aided Systems Theory – EUROCAST 2011, 600–607. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27549-4_77.
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Pihlak, René, and Andri Riid. "Simultaneous Road Edge and Road Surface Markings Detection Using Convolutional Neural Networks." In Communications in Computer and Information Science, 109–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57672-1_9.
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Novik, Anatoly, Igor Drozdetskiy, Pavel Petukhov, Nikita Labusov, Vasilina Novik, and Arina Popova. "Justification Constructions of the Road Pavement Under Conditions of Changing Road Surface Temperature." In Lecture Notes in Civil Engineering, 161–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72404-7_17.
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Aravindkumar, S., P. Varalakshmi, and Chindhu Alagappan. "Automatic Road Surface Crack Detection Using Deep Learning Techniques." In Artificial Intelligence and Technologies, 37–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6448-9_4.
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Ng, Jin Ren, Jan Shao Wong, Vik Tor Goh, Wen Jiun Yap, Timothy Tzen Vun Yap, and Hu Ng. "Identification of Road Surface Conditions using IoT Sensors and Machine Learning." In Lecture Notes in Electrical Engineering, 259–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2622-6_26.
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Heng, Hao, and Huilin Xiong. "Pedestrian Detection Based on Road Surface Extraction in Pedestrian Protection System." In Lecture Notes in Electrical Engineering, 793–800. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01273-5_88.
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Ito, Takanori, Akira Sakuraba, and Yoshitaka Shibata. "Development of Decision Algorithm for Road Surface Conditions by Crowd Sensing Technology." In Lecture Notes on Data Engineering and Communications Technologies, 361–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02613-4_32.
Тези доповідей конференцій з теми "Detection of road surface conditions":
1
Lu Junhui and Wang Jianqiang. "Road surface condition detection based on road surface temperature and solar radiation." In 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE 2010). IEEE, 2010. http://dx.doi.org/10.1109/cmce.2010.5610255.
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Lin, Paul P., Maosheng Ye, and Kuo-Ming Lee. "Intelligent observer-based road surface condition detection and identification." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811665.
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Nakagawa, Kazuya, Keiji Shibata, and Yuukou Horita. "Detection of road surface conditions by using an omni-directional camera and polarization properties." In 2011 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2011. http://dx.doi.org/10.1109/icce.2011.5722741.
4
Phanomchoeng, Gridsada, and Sunhapos Chantranuwathana. "Road Surface Condition Detection in Bicycle for Active Safety Applications." In The 13th International Conference on Automotive Engineering. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2017. http://dx.doi.org/10.4271/2017-01-1730.
5
Yang, Hun-Jun, Hyeok Jang, and Dong-Seok Jeong. "Detection algorithm for road surface condition using wavelet packet transform and SVM." In 2013 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2013). IEEE, 2013. http://dx.doi.org/10.1109/fcv.2013.6485514.
6
Fukuoka, Tomotaka, Takahiro Minami, Makoto Fujiu, and Junichi Takayama. "Study of filming condition for deep learning based crack detection method." In 6th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2021. http://dx.doi.org/10.5592/co/cetra.2020.1059.
Анотація:
Recently, the study of extending the service life of bridges has gained attention. In Japan, there are about 730,000 bridges with a length of 2 m or longer, and many of these were built during a period of high economic growth, and have now reached the end of their service life. Therefore, their rebuilding and the extension of their service life must be considered. However, some local public organizations have problems that insufficient manpower relative to the number of bridges to manage, as well as insufficient funding for maintenance. Thus, these organizations are unable to perform routine close visual inspections. Specific problems include “notably less staff and consulting technicians relative to the number of bridges to be managed” and “high inspection cost preventing from funding for repair.” As issues with the continuing close visual inspection of bridges are surfacing, the remote imaging system is expected to become a new inspection method that replaces close visual inspection. The practical potential of bridge inspections using images captured with a super-high-resolution camera was examined. A super-high-resolution camera enables us to take a wide area picture of a target bridge from a long distance. An image processing method could improve the efficiency of image-based inspection method. For example, a deep learning-based image processing method could extract a damaged area on a surface of a bridge automatically with high accuracy faster than human inspection. In general, the accuracy of an image processing method is affected by the quality of an input image. Filming conditions are one of the factors that determine the quality of a photo image. It is important to evaluate the effect of filming conditions to improve the reliability of an image processing method. In this paper, we evaluate the effect of the filming conditions for an image processing method by comparing the results of a deep learning-based crack detection method.
7
Li, Kang, James A. Misener, and Karl Hedrick. "On-Board Road Condition Monitoring System Using Slip-Based Tire-Road Friction Estimation and Wheel Speed Signal Analysis." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14102.
Анотація:
This paper presents an on-board road condition monitoring system developed for the safety application in Vehicle Infrastructure Integration (VII) project. The system equipped on the so-called probe vehicle is able to continuously evaluate road surface in terms of slipperiness and coarseness. Road surface is classified into four grades using stock mobile sensors and GPS speed-based measurements. The task of distinguishing slippery extents of road surfaces was treated as a "pattern-recognition" problem based on experimental results such that road surfaces can be classified into three slip levels, normal (μmax ≥0.5), slippery (0.3≥μmax <0.5), and very slippery (μmax <0.3) provided enough excitation. To distinguish rough road surfaces like gravel roads from normal asphalt roads, a separate classifier making use of a filterbank for analyzing wheel speed signal was implemented. Experimental results demonstrate the feasibility of this road condition monitoring system for detecting slippery and rough road surfaces in close to real-time. Once a slippery road condition is detected by the probe vehicle, a warning message with accurate GPS position can be transmitted from the probe vehicle to road side equipment (RSE) and further be relayed to following vehicles as well as traffic management center (TMC) via Dedicated Short Range Communication (DSRC); hence the safety of road users can be improved with the aid of this cooperative or VII active safety system.
8
Fukai, Hidekazu, Frederico Soares Cabral, Fernao A. L. Nobre Mouzinho, Vosco Pereira, and Satoshi Tamura. "The development of integrated road condition monitoring system for developing countries using smartphone sensors and dashcam in vehicles." In 6th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2021. http://dx.doi.org/10.5592/co/cetra.2020.1126.
Анотація:
In developing countries like Timor-Leste, regular road condition monitoring is a significant subject not only for maintaining road quality but also for a national plan of road network construction. The sophisticated equipment for road surface inspection is so expensive that it is difficult to introduce them in developing countries, and the monitoring is usually achieved by manual operation. On the other hand, the utilization of ICT devices such as smartphones has gained much attention in recent years, especially in developing countries because the penetration rate of the smartphone is remarkably increasing even in developing countries. The smartphones equip various high precision sensors, i.e., accelerometers, gyroscopes, GPS, and so on, in the small body in low price. In this project, we are developing an integrated road condition monitoring system that consists of smartphones, dashcams, and a server. There are similar trials in advanced countries but not so many in developing countries. This system assumes to be used in developing countries. The system is very low cost and does not require trained specialists in the field side. The items that are automatically inspected in this system were carefully selected with the local ministry of public works and include paved and unpaved classification, road roughness, road width, detection and size estimation of potholes, bumps, etc., at present. All the inspected items are visualized in Google Maps, Open Street Map, or QGIS with GPS information. The survey results are collected on a server and updated to more accurate values by the repeated surveys. On the analysis, we use several state-of-the-art machine learning and deep learning techniques. In this paper, we summarize related works and introduce this project’s target and framework, which especially focused on the developing countries, and achievements of each of our tasks.
9
Ameerali, Aaron, Nadine Sangster, and Gerard Ragbir. "AUTONOMOUS DETECTION OF VEHICULAR WHEEL ALIGNMENT PARAMETERS." In International Conference on Emerging Trends in Engineering & Technology (IConETech-2020). Faculty of Engineering, The University of the West Indies, St. Augustine, 2020. http://dx.doi.org/10.47412/boqw8777.
Анотація:
Vehicular technology has improved tremendously in the last few decades. Drivers and passengers are now being made more aware of their surroundings as well as the state of their cars, ergo becoming increasingly capable of making better decisions. These 'smart-vehicles' are directed by microcontrollers and microprocessors where a network of sensors and actuators provide contextual feedback for the user. Some of these features include parking and reverse assistance, collision avoidance and cruise control. In the coming years, this trend will undergo unprecedented growth as the technologies become cheaper to manufacture and implement. In fact, more advanced systems now alert the driver to realtime critical failures and problematic conditions while the simpler ones do so upon start-up. This paper provides a tested framework for a potential sensing system to alert the driver when the vehicle alignment is off. Vehicle misalignment can become an issue quickly as the following can result: Increased tire tread wear leading to reduced traction with the road's surface and ultimately higher chances of accidents as well as more frequent replacement of the tires becoming necessary. Uneven friction at contact between the road and tire can increase the resistance resulting in higher fuel consumption by the engine. Strain on multiple components within the braking system and suspension as misalignment can cause drift while in motion and additionally uneven braking. A damaged suspension is quite expensive to repair or replace. Early detection of the extent of misalignment can lead to decreased expenditure in the areas of maintenance and fuel consumption, contributing to an increase in reliability. Since many drivers, however experienced they are, may at times be ignorant of the degree of misalignment their vehicle possesses, adding this technology can serve as a potential remedy ultimately improving the user experience and vehicle longevity.
10
Takeuchi, Kazuya, Shohei Kawai, Keiji Shibata, and Yuukou Horita. "Distinction of winter road surface conditions using road surveillance camera." In 2012 12th International Conference on ITS Telecommunications (ITST). IEEE, 2012. http://dx.doi.org/10.1109/itst.2012.6425264.
Звіти організацій з теми "Detection of road surface conditions":
1
Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.
Анотація:
The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.
2
Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu, and Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.
Анотація:
The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center of INDOT developed a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and classification of vehicles involved in an incident. The research team designed the system, including the hardware and software components added to the existing INDOT CCTV system; the relationship between the added system and the currently existing INDOT system; the database structure for traffic data extracted from the videos; and a user-friendly, web-based server for showing the incident locations automatically. The specific work in this project includes vehicle-detection, road boundary detection, lane detection, vehicle count over time, flow-rate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The preliminary prototype of some system components has been implemented in the Development of Automated Incident Detection System Using Existing ATMS CCT (SPR-4305).
3
Weinschenk, Craig, Daniel Madrzykowski, and Paul Courtney. Impact of Flashover Fire Conditions on Exposed Energized Electrical Cords and Cables. UL Firefighter Safety Research Institute, October 2019. http://dx.doi.org/10.54206/102376/hdmn5904.
Анотація:
A set of experiments was conducted to expose different types of energized electrical cords for lamps, office equipment, and appliances to a developing room fire exposure. All of the cords were positioned on the floor and arranged in a manner to receive a similar thermal exposure. Six types of cords commonly used as power supply cords, extension cords, and as part of residential electrical wiring systems were chosen for the experiments. The non-metallic sheathed cables (NMB) typically found in residential electrical branch wiring were included to provide a link to previous research. The basic test design was to expose the six different types of cords, on the floor of a compartment to a growing fire to determine the conditions under which the cord would trip the circuit breaker and/or undergo an arc fault. All of the cords would be energized and installed on a non-combustible surface. Six cord types (18-2 SPT1, 16-3 SJTW, 12-2 NM-B, 12-3 NM-B, 18-3 SVT, 18-2 NISPT-2) and three types of circuit protection (Molded case circuit breaker (MCCB), combination Arc-fault circuit interrupter (AFCI), Ground-fault circuit interrupter (GFCI)) were exposed to six room-scale fires. The circuit protection was remote from the thermal exposure. The six room fires consisted of three replicate fires with two sofas as the main fuel source, two replicate fires with one sofa as the main fuel source and one fire with two sofas and MDF paneling on three walls in the room. Each fuel package was sufficient to support flashover conditions in the room and as a result, the impact on the cords and circuit protection was not significantly different. The average peak heat release rate of the sofa fueled compartment fires with gypsum board ceiling and walls was 6.8 MW. The addition of vinyl covered MDF wall paneling on three of the compartment walls increased the peak heat release rate to 12 MW, although most of the increased energy release occurred outside of the compartment opening. In each experiment during post flashover exposure, the insulation on the cords ignited and burned through, exposing bare conductor. During this period the circuits faulted. The circuit protection devices are not designed to provide thermal protection, and, thus, were installed remote from the fire. The devices operated as designed in all experiments. All of the circuit faults resulted in either a magnetic trip of the conventional circuit breaker or a ground-fault trip in the GFCI or AFCI capable circuit protection devices. Though not required by UL 1699, Standard for Safety for Arc-Fault Circuit-Interrupters as the solution for detection methodology, the AFCIs used had differential current detection. Examination of signal data showed that the only cord types that tripped with a fault to ground were the insulated conductors in non-metallic sheathed cables (12-2 NM-B and 12-3 NM-B). This was expected due to the bare grounding conductor present. Assessments of both the thermal exposure and physical damage to the cords did not reveal any correlation between the thermal exposure, cord damage, and trip type.
4
Desai, Jairaj, Jijo K. Mathew, Woosung Kim, Mingmin Liu, Howell Li, Jeffrey D. Brooks, and Darcy M. Bullock. Dashboards for Real-time Monitoring of Winter Operations Activities and After-action Assessment. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317252.
Анотація:
The Indiana Department of Transportation (INDOT) operates a fleet of nearly 1100 snowplows and spends up to $60M annually on snow removal and de-icing as part of their winter operation maintenance activities. Systematically allocating resources and optimizing material application rates can potentially save revenue that can be reallocated for other roadway maintenance operations. Modern snowplows are beginning to be equipped with a variety of Mobile Road Weather Information Sensors (MARWIS) which can provide a host of analytical data characterizing on-the-ground conditions during periods of wintry precipitation. Traffic speeds fused with road conditions and precipitation data from weather stations provide a uniquely detailed look at the progression of a winter event and the performance of the fleet. This research uses a combination of traffic speeds, MARWIS and North American Land Data Assimilation System (NLDAS) data to develop real-time dashboards characterizing the impact of precipitation and pavement surface temperature on mobility. Twenty heavy snow events were identified for the state of Indiana from November 2018 through April 2019. Two particular instances, that impacted 182 miles and 231 miles of interstate at their peaks occurred in January and March, respectively, and were used as a case study for this paper. The dashboards proposed in this paper may prove to be particularly useful for agencies in tracking fleet activity through a winter storm, helping in resource allocation and scheduling and forecasting resource needs.
5
Dahal, Sachindra, and Jeffery Roesler. Passive Sensing of Electromagnetic Signature of Roadway Material for Lateral Positioning of Vehicle. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-039.
Анотація:
Autonomous vehicles (AV) and advanced driver-assistance systems (ADAS) offer multiple safety benefits for drivers and road agencies. However, maintaining the lateral position of an AV or a vehicle with ADAS within a lane is a challenge, especially in adverse weather conditions when lane markings are occluded. For significant penetration of AV without compromising safety, vehicle-to-infrastructure sensing capabilities are necessary, especially during severe weather conditions. This research proposes a method to create a continuous electromagnetic (EM) signature on the roadway, using materials compatible with existing paving materials and construction methods. Laboratory testing of the proposed concept was performed on notched concrete-slab specimens and concrete prisms containing EM materials. An induction-based eddy-current sensor and magnetometers were implemented to detect the EM signature. The detected signals were compared to evaluate the effects of sensor height above the concrete surface, type of EM materials, EM-material volume, material shape, and volume of EM concrete prisms. A layer of up to 2 in. (5.1 cm) of water, ice, snow, or sand was placed between the sensor and the concrete slab to represent adverse weather conditions. Results showed that factors such as sensor height, EM-material volume, EM dosage, types of the EM material, and shape of the EM material in the prism were significant attenuators of the EM signal and must be engineered properly. Presence of adverse surface conditions had a negligible effect, as compared to normal conditions, indicating robustness of the presented method. This study proposes a promising method to complement existing sensors’ limitations in AVs and ADAS for effective lane-keeping during normal and adverse weather conditions with the help of vehicle-to-pavement interaction.
6
Clausen, Jay, Michael Musty, Anna Wagner, Susan Frankenstein, and Jason Dorvee. Modeling of a multi-month thermal IR study. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41060.
Анотація:
Inconsistent and unacceptable probability of detection (PD) and false alarm rates (FAR) due to varying environmental conditions hamper buried object detection. A 4-month study evaluated the environmental parameters impacting standoff thermal infra-red(IR) detection of buried objects. Field observations were integrated into a model depicting the temporal and spatial thermal changes through a 1-week period utilizing a 15-minute time-step interval. The model illustrates the surface thermal observations obtained with a thermal IR camera contemporaneously with a 3-d presentation of subsurface soil temperatures obtained with 156 buried thermocouples. Precipitation events and subsequent soil moisture responses synchronized to the temperature data are also included in the model simulation. The simulation shows the temperature response of buried objects due to changes in incoming solar radiation, air/surface soil temperature changes, latent heat exchange between the objects and surrounding soil, and impacts due to precipitation/changes in soil moisture. Differences are noted between the thermal response of plastic and metal objects as well as depth of burial below the ground surface. Nearly identical environmental conditions on different days did not always elicit the same spatial thermal response.
7
Aalto, Juha, and Ari Venäläinen, eds. Climate change and forest management affect forest fire risk in Fennoscandia. Finnish Meteorological Institute, June 2021. http://dx.doi.org/10.35614/isbn.9789523361355.
Анотація:
Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.
8
Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
Анотація:
The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,