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Relevant bibliographies by topics / Deep learning, computer vision, safety, road scene understanding / Journal articles

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Journal articles on the topic 'Deep learning, computer vision, safety, road scene understanding'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

Trabelsi, Rim, Redouane Khemmar, Benoit Decoux, Jean-Yves Ertaud, and Rémi Butteau. "Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical Survey." Sensors 22, no. 7 (2022): 2654. http://dx.doi.org/10.3390/s22072654.

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On-road behavior analysis is a crucial and challenging problem in the autonomous driving vision-based area. Several endeavors have been proposed to deal with different related tasks and it has gained wide attention recently. Much of the excitement about on-road behavior understanding has been the labor of advancement witnessed in the fields of computer vision, machine, and deep learning. Remarkable achievements have been made in the Road Behavior Understanding area over the last years. This paper reviews 100+ papers of on-road behavior analysis related work in the light of the milestones achie

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TS, Prof Nishchitha. "Real Time Object Detection in Autonomous Vehicle Using Yolo V8." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48914.

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Abstract Autonomous vehicles rely heavily on real-time object detection to ensure safe and efficient navigation in dynamic environments. This paper explores the implementation of YOLOv8 (You Only Look Once, version 8), a state-of-the-art deep learning model for object detection, within autonomous driving systems. YOLOv8 offers enhanced speed, accuracy, and lightweight deployment capabilities compared to its predecessors, making it highly suitable for real-time applications. The model is trained and evaluated on datasets such as KITTI and COCO to detect and classify various objects including pe

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Prasoona, Samala. "FogNet: An Enhanced Object Detection Model for Vehicle and Human Recognition in Foggy Conditions." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49854.

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Abstract : Foggy weather makes it really hard for vehicle detection systems to work properly. The visibility drops, and objects on the road become hard to recognize. To tackle this problem, we developed a smart and lightweight detection method based on an improved version of the YOLOv10 model. This system doesn't just rely on raw images-it first applies a series of advanced preprocessing techniques. These include data transformations, Dehaze Formers, and dark channel methods that help clean up the foggy images and bring out the important details. By doing this, we reduce the effect of haze and

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Deepa Mane, Et al. "A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 2027–38. http://dx.doi.org/10.17762/ijritcc.v11i10.8886.

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Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, t

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Jose, Mekha, Joshy Avin, R. Paleri Abishek, Mohan Athul, and Jasim R. M. Ali. "A Review on Contribution and Influence of Artificial Intelligence in Road Safety and Optimal Routing." International Journal on Emerging Research Areas (IJERA) 04, no. 02 (2025): 56–60. https://doi.org/10.5281/zenodo.14669339.

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Pothole detection is crucial for road safety and maintenance, driving research towards automated and efficient detection systems. Traditional methods present limitations: public reporting, while cost-effective, relies on citizen participation and lacks real-time information; vibration-based methods, using accelerometers to detect vehicle vibrations, require driving over potholes. Image/video processing techniques offer a proactive approach by analysing visual data to identify potholes. These methods often leverage computer vision algorithms, 3D scene reconstruction, and machine learning strate

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Samo, Madiha, Jimiama Mosima Mafeni Mase, and Grazziela Figueredo. "Deep Learning with Attention Mechanisms for Road Weather Detection." Sensors 23, no. 2 (2023): 798. http://dx.doi.org/10.3390/s23020798.

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There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous o

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Pavel, Monirul Islam, Siok Yee Tan, and Azizi Abdullah. "Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review." Applied Sciences 12, no. 14 (2022): 6831. http://dx.doi.org/10.3390/app12146831.

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In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for devel

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Hindarto, Djarot. "Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification." sinkron 8, no. 4 (2023): 2810–18. http://dx.doi.org/10.33395/sinkron.v8i4.13124.

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Recent computer vision and deep learning breakthroughs have improved road safety by automatically classifying traffic signs. This research uses CNNs to classify traffic signs to improve road safety. Autonomous vehicles and intelligent driver assistance systems require accurate traffic sign detection and classification. Using deep learning, we created a CNN model that can recognize and classify road traffic signs. This research uses a massive dataset of labeled traffic sign photos for training and validation. These CNN algorithms evaluate images and produce real-time predictions to assist drive

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Xu, Zhaosheng, Zhongming Liao, Xiaoyong Xiao, Suzana Ahmad, Norizan Mat Diah, and Azlan Ismail. "Target image detection algorithm of complex road scene based on improved multi-scale adaptive feature fusion technology." International Journal for Simulation and Multidisciplinary Design Optimization 16 (2025): 6. https://doi.org/10.1051/smdo/2025004.

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Understanding road scenes is crucial to the safe driving of autonomous vehicles, and object detection in road scenes is necessary to develop driving assistance systems. Current object detection algorithms are not very good at handling complex road scenes, and public datasets do not always adequately represent city traffic. Using Improved Multi-Scale Adaptive Feature Fusion Technology (IMSAFFT), this work suggests a real-time traffic information identification method to fix the issues of low detection accuracy of road scenes and high false detection rates in panoramic video images. In addition,

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Kherraki, Amine, Shahzaib Saqib Warraich, Muaz Maqbool, and Rajae El Ouazzani. "Residual balanced attention network for real-time traffic scene semantic segmentation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3281. http://dx.doi.org/10.11591/ijece.v13i3.pp3281-3289.

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<span lang="EN-US">Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vis

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Mohamed, Mohamed Gomaa, and Nicolas Saunier. "Behavior Analysis Using a Multilevel Motion Pattern Learning Framework." Transportation Research Record: Journal of the Transportation Research Board 2528, no. 1 (2015): 116–27. http://dx.doi.org/10.3141/2528-13.

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The increasing availability of video data, through existing traffic cameras or dedicated field data collection, and the development of computer vision techniques pave the way for the collection of massive data sets about the microscopic behavior of road users. Analysis of such data sets helps in understanding normal road user behavior and can be used for realistic prediction of motion and computation of surrogate safety indicators. A multilevel motion pattern learning framework was developed to enable automated scene interpretation, anomalous behavior detection, and surrogate safety analysis.

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Sreerambabu, Dr J., Mr N. Santhosh, Mr D. Rajkumar, and Ms K. Dharshini. "Drivable Road Region Segmentation in Real Time with High Precision using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 389–94. http://dx.doi.org/10.22214/ijraset.2023.54991.

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Abstract: This paper presents a novel approach that addresses the challenging task of real-time drivable road region extraction in computer vision. Semantic segmentation, which involves accurately identifying and segmenting objects in real-time data, is a complex problem. However, deep learning has proven to be a powerful technique for achieving semantic segmentation by automatically identifying patterns without the need for explicit programming. To tackle this task, the paper proposes a fusion of the YOLO algorithm and UNET architecture, leveraging their respective strengths. The YOLO algorit

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Amine, Kherraki, Saqib Warraich Shahzaib, Maqbool Muaz, and El Ouazzani Rajae. "Residual balanced attention network for real-time traffic scene semantic segmentation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3281–89. https://doi.org/10.11591/ijece.v13i3.pp3281-3289.

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Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic

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Mauri, Antoine, Redouane Khemmar, Benoit Decoux, Madjid Haddad, and Rémi Boutteau. "Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility." Journal of Imaging 7, no. 8 (2021): 145. http://dx.doi.org/10.3390/jimaging7080145.

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For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localiz

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Lee, Dong-Gyu. "Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant." Applied Sciences 11, no. 22 (2021): 10713. http://dx.doi.org/10.3390/app112210713.

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Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder–decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding

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Gajjar, Henil, and Stavan Sanyal. "AN IN-DEPTH STUDY OF LANE DETECTION FOR AUTONOMOUS CARS USING COMPUTER VISION TECHNIQUES." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (2023): 230–42. http://dx.doi.org/10.33564/ijeast.2023.v08i02.035.

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The article discusses the importance of selfdriving cars to improve road safety and reduce the number of accidents caused by human error. Self-driving cars not only reduce human error but also help reduce driver fatigue. We further explore the use of computer vision in autonomous cars, with previous research relying on deep learning algorithms with LiDAR sensors which can be expensive. The authors propose a more cost-effective approach using simple computer vision algorithms such as color space transformation, Canny edge detection, and Hough line transformation to detect lane lines and steer t

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Garg, Prateek, Anirudh Srinivasan Chakravarthy, Murari Mandal, Pratik Narang, Vinay Chamola, and Mohsen Guizani. "ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–18. http://dx.doi.org/10.1145/3418205.

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Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, and so on, which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, and so

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Ibrahim, Mohamed, James Haworth, and Tao Cheng. "WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning." ISPRS International Journal of Geo-Information 8, no. 12 (2019): 549. http://dx.doi.org/10.3390/ijgi8120549.

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Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that ad

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Tahir, Noor Ul Ain, Zuping Zhang, Muhammad Asim, Junhong Chen, and Mohammed ELAffendi. "Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches." Algorithms 17, no. 3 (2024): 103. http://dx.doi.org/10.3390/a17030103.

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Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consi

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Devkate, Rahul. "Vehicle Accident Detection and Alert System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50019.

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Abstract Road accidents constitute one of the leading causes of death and injuries in the global scene, and hence the requirement for developing efficient and real-time accident detection systems is great. This paper considers a vehicle accident detection system using computer vision techniques powered by OpenCV. Such a system utilises live video feeds that are acquired through surveillance cameras for monitoring vehicular movement with regard to accident detection due to sudden changes in velocity, direction, or collision events. It introduces features, including motion tracking, object detec

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Gite, Shilpa, Ketan Kotecha, and Gheorghita Ghinea. "Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment." International Journal of Pervasive Computing and Communications ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/ijpcc-11-2020-0192.

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Purpose This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques. Design/methodology/approach Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in vario

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"A Review On State Of The Art Abnormal Activity Recognition Approaches." International Journal of Emerging Trends in Engineering Research 9, no. 3 (2021): 182–88. http://dx.doi.org/10.30534/ijeter/2021/05932021.

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In last few decades, technological revolution has accelerated the deployment of large scale surveillance systems on almost all public places such as malls, hospitals, airports, railways, bus stations, roads, etc. These intelligent surveillance systems can play crucial role in governance of situations, collective security and safety, mitigating as well as prevention of adversaries. With gradual increase in multi camera surveillance systems enclosing multi angle views of same as well as different scenes has increased complexity of monitoring the systems by manual inspection. Abnormalities also k

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Bhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, and Fahmid Al Farid. "Video analytics using deep learning for crowd analysis: a review." Multimedia Tools and Applications, March 29, 2022. http://dx.doi.org/10.1007/s11042-022-12833-z.

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AbstractGathering a large number of people in a shared physical area is very common in urban culture. Although there are limitless examples of mega crowds, the Islamic religious ritual, the Hajj, is considered as one of the greatest crowd scenarios in the world. The Hajj is carried out once in a year with a congregation of millions of people when the Muslims visit the holy city of Makkah at a given time and date. Such a big crowd is always prone to public safety issues, and therefore requires proper measures to ensure safe and comfortable arrangement. Through the advances in computer vision ba

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Shaik, Allabaksh, and Shaik Mahaboob Basha. "Optimal deep transfer learning enabled object detector for anomaly recognition in pedestrian ways." Intelligent Decision Technologies, March 12, 2024, 1–16. http://dx.doi.org/10.3233/idt-240040.

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Anomaly detection is a branch of behavior understanding in surveillance scenes, where anomalies represent a deviation in the behavior of scene entities (viz.,humans, vehicles, and environment) from regular patterns. In pedestrian walkways, this plays a vital role in enhancing safety. With the widespread use of video surveillance systems and the escalating video volume, manual examination of abnormal events becomes time-intensive.Hence, the need for an automated surveillance system adept at anomaly detection is crucial, especially within the realm of computer vision (CV) research. The surge in

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Batur, Alp AKGÜL, ALİSİNANOĞLU Fatih, Sercan BAYRAM Kadir, and Sadettin ÖZYAZICI Mustafa. "DEVELOPMENT OF REAL-TIME TRAFFIC SIGN RECOGNITIONWITH CONVOLUTIONAL NEURAL NETWORK USING DEEP LEARNING TECHNIQUES." October 18, 2022. https://doi.org/10.5281/zenodo.7220943.

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Traffic signs are a mandatory feature of road traffic regulations worldwide. They are responsible for slowing down the speed and carrying out many other valuable duties, notifying drivers about dangerous parts of the route, signaling traffic destination, prohibiting, or allowing passage. In this way, traffic is smoother, it becomes better regulated and drivers understand, mark, and interpret the rules well. For this purpose, Machine Learning (ML) study is carried out with the Deep Learning (DL) approach, the Real-Time (RT) Traffic Signs Recognition (TSR) is successfully developed, and a 99,68%

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"A New Hybrid Proposed Algorithm for Multiple Vehicle Detection and Tracking in a Day-Time Environment." International Journal of Recent Technology and Engineering 8, no. 2 (2019): 457–69. http://dx.doi.org/10.35940/ijrte.b1526.078219.

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Multiple Vehicle detection and tracking is one of the hot research topics in the field of intelligent transportation systems, image processing, computer vision, robotics whereas applications are real time traffic monitoring, lane estimation, accident avoidance, alarm signal to indicate road accidents to save the public safety and so on. There exists a numerous higher level applications are motivated by a young researchers and scientists to identify the newly advanced techniques in which to solve the real time traffic problems using machine learning and deep learning methods to track multiple v

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Gutiérrez-Zaballa, Jon, Koldo Basterretxea, and Javier Echanobe. "Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach." ACM Transactions on Embedded Computing Systems, July 16, 2025. https://doi.org/10.1145/3748722.

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The use of hyperspectral imaging (HSI) for autonomous navigation is a promising field of research that aims to improve the accuracy and robustness of detection, tracking, and scene understanding systems based on vision sensors. The combination of advanced computer algorithms, such as deep neural networks (DNNs), and small-size snapshot HSI cameras allows to strengthen the reliability of those vision systems. Using HSI, some intrinsic limitations of greyscale and RGB imaging in depicting physical properties of targets related to the spectral reflectance of materials (metamerism) are overcome. D

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Verma, Rabindra Kumar. "Book Review." East European Journal of Psycholinguistics 7, no. 1 (2020). http://dx.doi.org/10.29038/eejpl.2020.7.1.kum.

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Susheel Kumar Sharma’s Unwinding Self: A Collection of Poems. Cuttack: Vishvanatha Kaviraj Institute, 2020, ISBN: 978-81-943450-3-9, Paperback, pp. viii + 152. Like his earlier collection, The Door is Half Open, Susheel Kumar Sharma’s Unwinding Self: A Collection of Poems has three sections consisting of forty-two poems of varied length and style, a detailed Glossary mainly on the proper nouns from Indian culture and tradition and seven Afterwords from the pens of the trained readers from different countries of four continents. The structure of the book is circular. The first poem “Snaps

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