Bibliografías temáticas / Deep learning, computer vision, safety, road scene understanding

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Literatura académica sobre el tema "Deep learning, computer vision, safety, road scene understanding"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "Deep learning, computer vision, safety, road scene understanding"

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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Tesis sobre el tema "Deep learning, computer vision, safety, road scene understanding"

Schoen, Fabio. "Deep learning methods for safety-critical driving events analysis." Doctoral thesis, 2022. http://hdl.handle.net/2158/1260238.

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In this thesis, we propose to study the data of crash and near-crash events, collectively called safety-critical driving events. Such data include a footage of the event, acquired from a camera mounted inside the vehicle, and the data from a GPS/IMU module, i.e., speed, acceleration and angular velocity. First, we introduce a novel problem, that we call unsafe maneuver classification, that aims at classifying safety-critical driving events based on the maneuver that leads to the unsafe situation and we propose a two-stream neural architecture based on Convolutional Neural Networks that perfor

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Actas de conferencias sobre el tema "Deep learning, computer vision, safety, road scene understanding"

Manfio Barbosa, Felipe, and Fernando Santos Osório. "3D Perception for Autonomous Mobile Robots Navigation Using Deep Learning for Safe Zones Detection: A Comparative Study." In Computer on the Beach. Universidade do Vale do Itajaí, 2021. http://dx.doi.org/10.14210/cotb.v12.p072-079.

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Computer vision plays an important role in intelligent systems, particularly for autonomous mobile robots and intelligent vehicles. It is essential to the correct operation of such systems, increasing safety for users/passengers and also for other people in the environment. One of its many levels of analysis is semantic segmentation, which provides powerful insights in scene understanding, a task of utmost importance in autonomous navigation. Recent developments have shown the power of deep learning models applied to semantic segmentation. Besides, 3D data shows up as a richer representation o

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