Relevant bibliographies by topics / Object detection accuracy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Object detection accuracy'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Object detection accuracy"

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Wang, Zhiyuan, Shujun Men, Yuntian Bai, et al. "Improved Small Object Detection Algorithm CRL-YOLOv5." Sensors 24, no. 19 (2024): 6437. http://dx.doi.org/10.3390/s24196437.

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Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module i
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Zhou, Lei, and Jingke Xu. "Enhanced Abandoned Object Detection through Adaptive Dual-Background Modeling and SAO-YOLO Integration." Sensors 24, no. 20 (2024): 6572. http://dx.doi.org/10.3390/s24206572.

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Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adju
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Liu, Zhiguo, Enzheng Zhang, Qian Ding, Weijie Liao, and Zixiang Wu. "An Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s." Sensors 25, no. 1 (2024): 85. https://doi.org/10.3390/s25010085.

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Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the
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Dawn, Wilson, Manusankar C. Dr., and Prathibha P. H. Dr. "Analytical Study on Object Detection using Yolo Algorithm." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 587–89. https://doi.org/10.5281/zenodo.7036535.

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Object detection is a technique that allows detecting and locating objects in videos and images. Object detection is widely used to count objects in a scene, track their precise locations and accurately label the objects. It seeks to answer what is the object? and Where is it? . Object detection adopts various approaches such as fast R-CNN, Retina-Net, Single Shot MultiBox Detector (SSD) and YOLO. Among these, YOLO is the most powerful algorithm for object detection and as well as suited for real-time scenarios. It is popular because of its accuracy and speed. YOLO uses Neural networks to prov
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S. Hasan, Athraa, Jianjun Yi, Haider M. AlSabbagh, and Liwei Chen. "Multiple Object Detection-Based Machine Learning Techniques." Iraqi Journal for Electrical and Electronic Engineering 20, no. 1 (2024): 149–59. http://dx.doi.org/10.37917/ijeee.20.1.15.

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Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic
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Ogunrinde, Isaac, and Shonda Bernadin. "Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions." Sensors 23, no. 14 (2023): 6255. http://dx.doi.org/10.3390/s23146255.

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AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained
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K, Chidananda, Maloth Gulshan Naik, Yama Mohan, Neerati Madhavan, Sheik Afzal Arfan, and Ashish Kativarapu. "An efficient novel paradigm for object detection through web camera using deep learning (YOLOv5’s object detection model)." E3S Web of Conferences 391 (2023): 01093. http://dx.doi.org/10.1051/e3sconf/202339101093.

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Object detection, a fundamental duty in computer vision that has a wide range of practical applications, they are surveillance, robotics, and autonomous driving. Recent developments of deep learning have got gradual improvemenrts in detection accuracy and speed. One of the most popular and effective deep learning models for object detection is YOLOv5. In this discussion, we an object detection model through YOLOv5 and its implementation for object detection tasks. We discuss the model’s architecture, training process, and evaluation metrics. Furthermore, we present experimental results on popu
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Meng, Jintao, Shaokai Shen, Jiaqi Wang, and Chunjian Zhou. "Object Detection Algorithms Based on Deep Learning: A Review." Asian Journal of Research in Computer Science 17, no. 7 (2024): 145–56. http://dx.doi.org/10.9734/ajrcos/2024/v17i7485.

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With the continuous development of deep learning, object detection algorithms based on deep learning have made significant progress in the field of computer vision, widely applied in areas such as autonomous driving, industrial inspection, agriculture, transportation, and medicine. Traditional object detection algorithms face issues such as low detection efficiency and poor robustness. However, deep learning-based object detection algorithms significantly enhance detection accuracy and generalization by learning low-level and high-level image features. This article first introduces traditional
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Xu, Xin, Bin Liu, Yun Zhong, and Song Hua. "Lightweight Detection Algorithm for Air-to-Ground Threat Object." Journal of Physics: Conference Series 2872, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1742-6596/2872/1/012032.

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Abstract In air-to-ground threat object detection, the typically strong concealment, small size, and high movement speed of threat objects often lead to challenges such as missed detections, low accuracy, and an excessive number of network parameters. An efficient lightweight detection algorithm for air-to-ground threat objects is proposed, built upon the YOLOv7-tiny framework. To address the issue of large network parameters, lightweight modules ELAN-GS and Slim-Neck are employed. Additionally, to refine the detection precision for small objects, the NWD metric and NWD loss function are intro
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Ting-Na Liu, Ting-Na Liu, Zhong-Jie Zhu Ting-Na Liu, Yong-Qiang Bai Zhong-Jie Zhu, Guang-Long Liao Yong-Qiang Bai, and Yin-Xue Chen Guang-Long Liao. "YOLO-Based Efficient Vehicle Object Detection." 電腦學刊 33, no. 4 (2022): 069–79. http://dx.doi.org/10.53106/199115992022083304006.

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<p>Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for accuracy and real-time. However, the existing algorithms suffer from the contradiction between detec-tion speed and detection accuracy, and weak generalization ability. To address these issues, an improved vehicle detection algorithm is presented based on the You Only Look Once (YOLO). On the one hand, an efficient feature extraction network is restructured to speed up the feature transfer of the object, and re-use the feature information extracted from the input image. On th
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Dissertations / Theses on the topic "Object detection accuracy"

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Yadav, Kamna. "Improving Accuracy of the Edgebox Approach." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7326.

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Object region detection plays a vital role in many domains ranging from self-driving cars to lane detection, which heavily involves the task of object detection. Improving the performance of object region detection approaches is of great importance and therefore is an active ongoing research in Computer Vision. Traditional sliding window paradigm has been widely used to identify hundreds of thousands of windows (covering different scales, angles, and aspect ratios for objects) before the classification step. However, it is not only computationally expensive but also produces relatively low acc
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Yu, Ying. "Improving the Accuracy of 2D On-Road Object Detection Based on Deep Learning Techniques." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235194.

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This paper focuses on improving the accuracy of detecting on-road objects, includingcars, trucks, pedestrians, and cyclists. To meet the requirements of theembedded vision system and maintain a high speed of detection in the advanceddriving assistance system (ADAS) domain, the neural network model is designedbased on single channel images as input from a monocular camera.In the past few decades, forward collision avoidance system, a sub-system ofADAS, has been widely adopted in vehicular safety systems for its great contributionin reducing accidents. Deep neural networks, as the the-state-of-a
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Ye, Fanjie. "A Method of Combining GANs to Improve the Accuracy of Object Detection on Autonomous Vehicles." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1752364/.

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As the technology in the field of computer vision becomes more and more mature, the autonomous vehicles have achieved rapid developments in recent years. However, the object detection and classification tasks of autonomous vehicles which are based on cameras may face problems when the vehicle is driving at a relatively high speed. One is that the camera will collect blurred photos when driving at high speed which may affect the accuracy of deep neural networks. The other is that small objects far away from the vehicle are difficult to be recognized by networks. In this paper, we present a meth
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Güven, Jakup. "Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-19999.

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I detta arbete genomförs utvecklingen av ett realtids objektdetekteringssystem för iOS. För detta ändamål används YOLO, en ett-stegs objektdetekterare och ett s.k. ihoplänkat neuralt nätverk vilket åstadkommer betydligt bättre prestanda än övriga realtidsdetek- terare i termer av hastighet och precision. En dörrdetekterare baserad på YOLO tränas och implementeras i en systemutvecklingsprocess. Maskininlärningsprocessen sammanfat- tas och praxis för att undvika överträning eller “overfitting” samt för att öka precision och hastighet diskuteras och appliceras. Vidare genomfo
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Du, Pisani Renaldo Murray. "Design of an Underwater Object Detection and Location System using Wide-Beam SONAR." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86236.

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Thesis (MScEng)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: This thesis describes the second project relating to the development of a SONAR (SOund Navigation And Ranging) object detection and collision avoidance system for use on an AUV (Autonomous Underwater Vehicle) at Stellenbosch University. The main goal is to develop and test techniques that make use of the existing SONAR laboratory platform and wide-beam SONAR transducers to detect and locate objects and their limits/bounds under water in the horizontal plane. The results of the work done show that it is possible to use w
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Pereira, Alex Lopes. "Accurate abandoned object detection (AOD) in surveillance video." Instituto Tecnológico de Aeronáutica, 2015. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=3230.

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The aim of the present research is to investigate the problem known as Abandoned Object Detection (AOD) in surveillance videos, where stationary objects must be classified as either abandoned or removed. We found four categories of methods to solve the AOD problem, namely, region growing, edge detection, color comparison and image inpainting and investigated all of them. We found the major drawback of each category, from which we derived guidelines that oriented the development of three novel methods. Among these three methods, the proposed method (based on edge detection) measures the ratio o
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Köylüoglu, Tugay, and Lukas Hennicks. "Evaluating rain removal image processing solutions for fast and accurate object detection." Thesis, KTH, Mekatronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254446.

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Autonomous vehicles are an important topic in modern day research, both for the private and public sector. One of the reasons why self-driving cars have not yet reached consumer market is because of levels of uncertainty. This is often tackled with multiple sensors of different kinds which helps gaining robust- ness in the vehicle’s system. Radars, lidars and cameras are often the sensors used and the expenses can rise up quickly, which is not always feasible for different markets. This could be addressed with using fewer, but more robust sensors for visualization. This thesis addresses the is
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Čírtek, Jiří. "Sledování malých změn objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217199.

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This diploma thesis inspects problems with specification location of edges with higher accuracy then one pixel (subpixel accuracy). In terms of this assignment has been created program, which generates three different shapes of objects. With change of parameters in program is measuring location of gravitational center on objects with subpixel accuracy. Obtained data of gravitational center deviations are depictured in graphs.
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Jensen, Anne. "The accuracy and precision of kinesiology-style manual muscle testing : designing and implementing a series of diagnostic test accuracy studies." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4fd95394-e812-402e-9195-6c82643eaa15.

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<b>Introduction</b>: Kinesiology-style manual muscle testing (kMMT) is a non-invasive assessment method used by various types of practitioners to detect a wide range of target conditions. It is distinctly different from the muscle testing performed in orthopaedic/neurological settings and from Applied kinesiology. Despite being estimated to be used by over 1 million people worldwide, the usefulness of kMMT has not yet been established. The aim of this thesis was to assess the validity of kMMT by examining its accuracy and precision. <b>Methods</b>: A series of 5 diagnostic test accuracy studie
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Follmann, Patrick Moritz [Verfasser], Carsten [Akademischer Betreuer] Steger, Carsten [Gutachter] Steger, and Bernt [Gutachter] Schiele. "Few-Shot Object Detection in Industrial Applications : Training Accurate Models with Few Annotations / Patrick Moritz Follmann ; Gutachter: Carsten Steger, Bernt Schiele ; Betreuer: Carsten Steger." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1240832788/34.

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Book chapters on the topic "Object detection accuracy"

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Staudenrausch, Tim, and Bernd Lüdemann-Ravit. "Cycle Time Measurement Using AI-Based Object Detection and Tracking in Industrial Processes." In ARENA2036. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88831-1_22.

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Abstract This paper presents an AI-based system for improving cycle time measurement in industrial environments, leveraging YOLOv8 for object detection and ByteTrack for tracking. Our non-invasive approach analyzes video from an Azure Kinect camera to calculate cycle times by detecting objects and monitoring their state changes. Tested at the University of Applied Sciences Kempten’s demo plant, the system showcased high accuracy against ground truth data, highlighting its potential to enhance production line monitoring and efficiency significantly. This work contributes to industrial automatio
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Brenner, Rorry, Jay Priyadarshi, and Laurent Itti. "Perfect Accuracy with Human-in-the-Loop Object Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48881-3_25.

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Sirala, Divya, and Kuldeep Singh Nagla. "Accuracy in Object Detection by Using Deep Learning Method." In Recent Advances in Metrology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4594-8_5.

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Chung, Youngsun, Daeyoung Gil, and Ghang Lee. "Optimal Number of Cue Objects for Photo-Based Indoor Localization." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.98.

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Building information modeling (BIM) is widely used to generate indoor images for indoor localization. However, changes in camera angles and indoor conditions mean that photos are much more changeable than BIM images. This makes any attempt at localization based on the similarity between real photos and BIM images challenging. To overcome this limitation, we propose a reasoning-based approach for determining the location of a photo by detecting the cue objects in the photo and the relationships between them. The aim of this preliminary study was to determine the optimal number of cue objects re
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Chung, Youngsun, Daeyoung Gil, and Ghang Lee. "Optimal Number of Cue Objects for Photo-Based Indoor Localization." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.98.

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Building information modeling (BIM) is widely used to generate indoor images for indoor localization. However, changes in camera angles and indoor conditions mean that photos are much more changeable than BIM images. This makes any attempt at localization based on the similarity between real photos and BIM images challenging. To overcome this limitation, we propose a reasoning-based approach for determining the location of a photo by detecting the cue objects in the photo and the relationships between them. The aim of this preliminary study was to determine the optimal number of cue objects re
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Chhatrband, Jyoti Vitthal, and BhaveshKumar Choithram Dharmani. "Efficiency and Accuracy Analysis of Object Detection Algorithms in Deep." In Intelligent Circuits and Systems for SDG 3 – Good Health and well-being. CRC Press, 2024. http://dx.doi.org/10.1201/9781003521716-79.

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Yoshitake, Hiroshi, Jinyu Gu, and Motoki Shino. "Occluded Area Detection Based on Sensor Fusion and Panoptic Segmentation." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_66.

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AbstractDetecting occluded areas in a driving environment is crucial to preventing traffic accidents against hidden road agents coming out from such occluded areas. Our previous work proposed a novel detection method that can offer geometric information of the detected areas by utilizing camera and LiDAR sensor fusion. However, it had difficulty identifying individual areas formed by different objects without information about distinct objects. Thus, the objective of this study was to improve our previous methodology, and panoptic segmentation, which can distinguish between individual objects
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Kang, Ming, Chee-Ming Ting, Fung Fung Ting, and Raphaël C. W. Phan. "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43901-8_57.

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Wang, Wenlong, and Pinyan Hua. "Enhancing Object Detection Accuracy with Hybrid Supervision and Trans-Stage Interaction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-96-0122-6_26.

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Zhang, Xingguo, Daiki Ikami, and Pongsathorn Raksincharoensak. "Robust 3D On-Road Object Detection and Distance Estimation for Active Vehicle Control Systems Based on Monocular Camera Image Data." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_65.

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Abstract3D object detection from monocular camera videos constitutes a critical research domain. Achieving robust 3D object detection in databases lacking annotated information poses a highly challenging task. This paper proposes a simple yet effective transfer learning approach, integrating data alignment, 3D object detection, and dynamic result correction. Vanishing point detection is employed to infer camera angles in diverse scenes, and preprocessing of new data is conducted by considering the camera's pitch angle and vanishing point position. Subsequently, MonoDETR are applied for depth e
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Conference papers on the topic "Object detection accuracy"

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Verma, Shivam Gopikishan, Sudhanshu Maurya, Himanshu Pant, Ajay Kumar Yadav, Sneha Thomas Varghese, and Rachit Garg. "YOLO: Redefining Real-Time Object Detection Accuracy and Efficiency." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10731055.

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Sheoran, Aparajita, Aman Tripathi, and Ankur Bharadwaj. "Comparative Analysis of Object Detection Accuracy in Diverse Background Conditions." In 2025 10th International Conference on Signal Processing and Communication (ICSC). IEEE, 2025. https://doi.org/10.1109/icsc64553.2025.10968927.

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Zhang, Xu, Chengwei Yang, Yuanfang Tu, Sheng Zhang, and Chang Liu. "Advances in UAV Object Detection: An Improved YOLOv8 Model for Superior Small Object Accuracy." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10840022.

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Patel, Sanskruti. "Hybrid CNN-Transformer for Aerial Object Detection: A Novel Architecture for Enhanced Detection Accuracy." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968057.

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Goyal, Himanshu Rai, Anurag Shrivastava, Krishna Kant Dixit, Amandeep Nagpal, B. Ravali Reddy, and Jaysheel Kumar. "Improving Accuracy of Object Detection in Autonomous Drones with Convolutional Neural Networks." In 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). IEEE, 2025. https://doi.org/10.1109/icccit62592.2025.10927983.

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Liu, Xiangyu, Haorui Zuo, Shenbo Zhou, and Jianlin Zhang. "Refined YOLOv5s for UAV-based real-time object detection with improved accuracy." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070766.

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Dong, Yuxuan, Patrice Delmas, and Mitchell Rogers. "Impact of Object Detector Accuracy on Tracking-By-Detection Methods: A Case Study with Meerkats." In 2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2024. https://doi.org/10.1109/ivcnz64857.2024.10794454.

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Singh, Raushan Kumar, Sudeepta Mishra, and S. Yayathi Pavan Kumar. "Undermining Live Feed ML Object Detection Accuracy with EMI on Vehicular Camera Sensors." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683002.

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Park, Seongmin, Minjae Lee, Junwon Choi, and Jungwook Choi. "Selectively Dilated Convolution for Accuracy-Preserving Sparse Pillar-based Embedded 3D Object Detection." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00810.

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Bian, Shizheng, Zhenhuan Zhao, Gai Lu, Yifan Zhang, and Yang Yu. "MB-RSVP: multi-brain can improve accuracy of single-trial EEG-based object detection." In Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), edited by Xiangjie Kong and Xingjian Wang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049652.

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Reports on the topic "Object detection accuracy"

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Mazari, Mehran, Yahaira Nava-Gonzalez, Ly Jacky Nhiayi, and Mohamad Saleh. Smart Highway Construction Site Monitoring Using Artificial Intelligence. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2336.

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Construction is a large sector of the economy and plays a significant role in creating economic growth and national development,and construction of transportation infrastructure is critical. This project developed a method to detect, classify, monitor, and track objects during the construction, maintenance, and rehabilitation of transportation infrastructure by using artificial intelligence and a deep learning approach. This study evaluated the performance of AI and deep learning algorithms to compare their performance in detecting and classifying the equipment in various construction scenes.
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Tao, Yang, Victor Alchanatis, and Yud-Ren Chen. X-ray and stereo imaging method for sensitive detection of bone fragments and hazardous materials in de-boned poultry fillets. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7695872.bard.

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As Americans become increasingly health conscious, they have increased their consumptionof boneless white and skinless poultry meat. To the poultry industry, accurate detection of bonefragments and other hazards in de-boned poultry meat is important to ensure food quality andsafety for consumers. X-ray imaging is widely used for internal material inspection. However,traditional x-ray technology has limited success with high false-detection errors mainly becauseof its inability to consistently recognize bone fragments in meat of uneven thickness. Today’srapid grow-out practices yield chicken bo
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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Pourhomayoun, Mohammad. Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI to Detect Near-Miss Collisions. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2350.

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Near-Miss Collisions are events that, with a slight change in position or timing, could have resulted in a collision, which could have caused severe injury or property damage. Understanding near-miss collisions can help identify risks and potentially improve road safety. In this project, we developed an effective end-to-end system based on advanced artificial intelligence (AI) models and computer vision algorithms to detect and report near-miss collisions as an important indicator to identify and measure safety risks, especially in specific circumstances such as a right turn on a red light. Th
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Panta, Manisha, Padam Thapa, Md Hoque, et al. Application of deep learning for segmenting seepages in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49453.

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Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To th
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Muldavin, Esteban, Yvonne Chauvin, Teri Neville, et al. A vegetation classi?cation and map: Guadalupe Mountains National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302855.

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A vegetation classi?cation and map for Guadalupe Mountains National Park (NP) is presented as part of the National Park Service Inventory &amp; Monitoring - Vegetation Inventory Program to classify, describe, and map vegetation communities in more than 280 national park units across the United States. Guadalupe Mountains NP lies in far west Texas and contains the highest point in the state, Guadalupe Peak (8,751 ft; 2,667 m). The mountain escarpments descend some 5,000 ft (1,500 m) to the desert basins below forming a complex geologic landscape that supports vegetation communities ranging from
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