Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 957-964· 0 citations· 15 references
Abstract
With the increasing number of vehicles, urbanization and the constant rise in road usage, traffic violations have become hugely problematic in today's transportation situations. Some of the most common dangerous driving behaviors that lead to road accidents and traffic delays are as follows: Not wearing a helmet, running a red light, and breaking lanes, breaking seatbelt, using a cell phone while driving and triple riding. Maintaining consistent observation, precise detection, scalability, and speedy identification of traffic infractions in complex road situations are all challenges faced by current traffic violation monitoring methods. Factors such as high traffic levels, uneven lighting, environmental interference, and requiring human supervision limit the effectiveness of current monitoring methods. Therefore, it becomes essential to have a sophisticated automated system that can efficiently do real-time traffic infraction analysis. The proposed study utilizes a novel Traffic violations identification method based on YOLOv8 to detect many traffic violations in the surveillance photos and videos. To achieve the system execution, a Traffic Rule-net Dataset was developed from a set of traffic data collected from different scenarios of city transport, highways and crossroads. The quality of the features and the robustness of the suggested model were enhanced using a number of data pre-processing techniques, including normalization, image size modification, data augmentation, and filtering. The new framework was applicable in all environmental conditions, allowing for efficient object localization and classification of different breaches. From the experimental evaluation it is clear that there was a reduction in false detection, performance, and detection efficiency. Infrancements of the traffic rules may be easily and efficiently detected for traffic control through the use of intelligent monitoring.
Motorcycle traffic accidents have been on the rise because of low compliance with helmet laws and there is need for automated traffic monitoring and enforcement systems. This paper proposes a traffic enforcement solution that combines YOLOv8 based object detection and PaddleOCR for automatic violation detection and license plate recognition of motorcyclists. The system will detect motorcycles, riders, helmets and license plates from traffic images, identify the helmet violation cases by analyzing their spatial relationship and extract the registration numbers of the vehicles for automatic enforcement. The solution is trained and tested using 4,169 traffic images that have been labeled. The helmet detection model obtains an mAP@50 score of 0.983, and the license plate detection module gives localization of the license plates which makes them recognizable by the OCR. There is a Flask based web app that allows users to upload images, detect violations, generate evidence, and notify fines.
Rapid urbanization and increasing vehicle numbers have led to a significant rise in road accidents, posing serious threats to human life and economic stability. Traditional accident detection systems rely on manual reporting, causing delays in emergency response. To address this, computer vision integrated with intelligent transportation systems offers an effective solution. This study analyzes pre-2018 approaches to traffic accident detection using video surveillance. The proposed system automatically detects accidents by analyzing traffic camera footage through feature extraction, motion analysis, and pattern recognition. It identifies abnormal vehicle behavior such as sudden speed drops, collisions, and irregular trajectories. Classical computer vision techniques like optical flow, background subtraction, edge detection, and machine learning methods such as Support Vector Machines (SVM) and decision trees are used. The system includes preprocessing, object detection, trajectory tracking, and classification of normal and abnormal events, supported by mathematical modeling and threshold-based decisions. Results show high detection accuracy with low false alarms, especially in controlled environments like highways and intersections. However, challenges remain in real-time implementation due to lighting, occlusion, and camera angle issues. Future work suggests incorporating deep learning and multi-sensor data fusion to improve performance. In conclusion, computer vision-based accident detection is a promising approach to enhancing road safety and reducing response time, contributing to the advancement of smart transportation systems.
Nimal Perera, Tharindu Jayasinghe· International Journal of Mod...· 0 citations
An effective real-time traffic accident detection framework based on YOLOv8 that can be implemented in intelligent transportation systems, traffic surveillance platforms, and advanced driver assistance applications is proposed.
Chuwe Ashlet Munashe, Chaoyu Yang· International Journal of Sci...· 0 citations
In recent years, traffic accidents on highways have occurred frequently. An accident threatens lives and property. It also disrupts traffic. Current accident scene recognition faces challenges. Accuracy is often suboptimal. Deploying detection models in real-world scenarios is difficult due to high computational complexity and insufficient inference speed, rather than parameter size alone. These scenarios have limited computational resources. High computational complexity and low inference efficiency cause the difficulty, while model parameter size is not the only bottleneck. To address these issues, a lightweight highway accident scene recognition algorithm based on an improved YOLOv8n is proposed. This algorithm is designed with potential deployment in fixed monitoring devices installed at accident-prone highway sections in mind. It enables automatic accident detection through real-time video analysis and could promptly send alerts to traffic management centers, potentially facilitating rapid dispatch of rescue resources and traffic control, thereby offering a pathway to enhance highway safety management and emergency response efficiency. However, we note that field deployment experiments and hardware-level latency tests are not included in this study, and thus practical applicability claims remain to be validated in future work. First, MobileViT, a lightweight CNN, enhances feature extraction for highway accident targets, showing improved robustness under simulated adverse weather conditions (e.g. blurring and contrast reduction that mimic fog and rain). However, we explicitly note that these are image-space simulations; the model’s generalization to authentic foggy or rainy environments has not been tested in this study and remains a critical direction for future investigation. Second, Ghostnet and VanillaNet are added to the model’s neck network, reducing computational complexity and improving inference speed while maintaining detection accuracy and keeping parameter growth minimal in extreme weather. Finally, SlideLoss replaces the original Loss function to address sample imbalance and improve detection of complex targets. Ablation and comparative experiments were conducted using the DADA and Car Crash Dataset datasets. The proposed algorithm achieves higher average precision than traditional methods. The improved model’s average precision increased by 2.3%, from 92.8% to 95.1%, while also adhering to lightweight design principles, with computational complexity reduced from 8.2 to 7.2 and the number of parameters decreased from 3.0 M to 2.9 M. These findings confirm the algorithm’s superiority and the effectiveness of its improvements.
Dengcong Mu, Zitang Wei, Zheng Li et al.· Engineering Research Express· 0 citations
Traffic sign detection is an integral part of Advanced Driver Assistance Systems (ADAS) and self-driving cars wherein correct and timely detection helps ensure a safer driving experience. However, currently available models are trained on benchmark datasets that pertain to European or Chinese traffic scenarios. Such models lack the efficiency required for traffic scenarios in India due to multiple factors such as the variety of sign shapes and languages present, occlusions, varied light intensities and prevalence of smaller objects. In this paper, a region-based traffic sign detection system has been proposed using the enhanced YOLOv8 framework for Indian traffic scenarios. A subset of the Mapillary Traffic Sign Dataset (MTSD) has been curated, annotated and data-augmented with mosaic augmentation, change in brightness, adaptive scaling, and high-resolution training to overcome challenges posed by occlusions, varying light intensity and distant traffic signs.
L. M, S. N., Sohan Js et al.· 2026 7th International Confe...· 0 citations
The developed and implementation of an AI-based traffic violation detection system designed to automate the monitoring of road traffic violations using CCTV footage and computer vision techniques provides a practical and innovative solution to contemporary traffic violation control and management challenges.
O. Famodimu, Mayowa Osundina, Dorachima Ifeanyi et al.· Asian Journal of Research in...· 0 citations
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