An enhanced YOLOv8-based framework for real-time traffic accident detection in dynamic road environments

Traffic accidents remain a crucial problem for intelligent transportation systems (ITS), and the ability to detect them quickly and efficiently is one of the keys to ensuring an appropriate emergency response and improving road safety. Traditional accident monitoring methods often suffer from poor detection accuracy, high computational costs, and reduced robustness when dealing with complex traffic conditions. This paper presents a novel enhanced YOLOv8-based framework for real-time traffic accident detection capable of operating in dynamic road environments.
The proposed framework uses YOLOv8 as the base detection model, which provides powerful feature extraction capabilities, an anchor-free detection mechanism, and a favorable trade-off between accuracy and inference speed. To address the challenges associated with detecting accident-related objects, such as occlusion, illumination variations, and complex traffic backgrounds, the proposed framework incorporates feature augmentation to enhance feature representation. Multi-scale features are fused at different scales to improve local details, thereby enabling the extraction of critical regional features.
Experimental results show that the proposed framework achieves competitive detection performance, with a precision of 94.3%, a recall of 92.7%, and an F1-score of 93.5%, as well as a mAP@0.5 of 96.1%. Furthermore, the proposed framework achieves a real-time inference speed of 58 FPS on the COCO test-dev 2017 dataset, compared with its own smaller YOLOv4-Nano model with an input size of 416 × 416 pixels. Qualitative results further demonstrate the ability of the proposed approach to localize traffic objects in both successful and direct accident scenarios.
In this paper, we propose 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.