Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5

• URL: http://arxiv.org/abs/2304.09248v2
• Date: Mon, 20 Nov 2023 19:48:39 GMT
• Title: Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5
• Authors: Elham Soltanikazemi, Ashwin Dhakal, Bijaya Kumar Hatuwal, Imad Eddine Toubal, Armstrong Aboah, Kannappan Palaniappan
• Abstract summary: This research focuses on real-time surveillance systems as a means for tackling the issue of non-compliance with helmet regulations. Previous attempts at real-time helmet violation detection have been hindered by their limited ability to operate in real-time. This paper introduces a novel real-time helmet violation detection system that utilizes the YOLOv5 single-stage object detection model.
• Score: 6.081363026350582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research focuses on real-time surveillance systems as a means for tackling the issue of non-compliance with helmet regulations, a practice that considerably amplifies the risk for motorcycle drivers or riders. Despite the well-established advantages of helmet usage, achieving widespread compliance remains challenging due to diverse contributing factors. To effectively address this concern, real-time monitoring and enforcement of helmet laws have been proposed as a plausible solution. However, previous attempts at real-time helmet violation detection have been hindered by their limited ability to operate in real-time. To overcome this limitation, the current paper introduces a novel real-time helmet violation detection system that utilizes the YOLOv5 single-stage object detection model. This model is trained on the 2023 NVIDIA AI City Challenge 2023 Track 5 dataset. The optimal hyperparameters for training the model are determined using genetic algorithms. Additionally, data augmentation and various sampling techniques are implemented to enhance the model's performance. The efficacy of the models is evaluated using precision, recall, and mean Average Precision (mAP) metrics. The results demonstrate impressive precision, recall, and mAP scores of 0.848, 0.599, and 0.641, respectively for the training data. Furthermore, the model achieves notable mAP score of 0.6667 for the test datasets, leading to a commendable 4th place rank in the public leaderboard. This innovative approach represents a notable breakthrough in the field and holds immense potential to substantially enhance motorcycle safety. By enabling real-time monitoring and enforcement capabilities, this system has the capacity to contribute towards increased compliance with helmet laws, thereby effectively reducing the risks faced by motorcycle riders and passengers.

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