Urban traffic environments pose significant challenges for automated vehicle detection, including fluctuating lighting, adverse weather, and complex road conditions. Visibility issues from fog, rain, and low light, alongside the prevalence of small vehicles in dense traffic, hinder detection accuracy. This study proposes an enhanced YOLOv5-based model for improved vehicle detection in complex urban traffic scenarios. Key contributions include integrating BiFPN for robust multi-scale feature fusion, adding an FFA module to boost detection under low-visibility conditions, and incorporating Image Adjustment Techniques (IAT) for preprocessing. Additionally, select YOLOv5 modules were upgraded to YOLOv8 components, yielding notable performance gains over the baseline model.
Vehicle detection in different traffic scenarios based on YOLOv5
International Conference on Computer Vision and Image Processing (CVIP 2024) ; 2024 ; Hangzhou, China
Proc. SPIE ; 13521
2025-01-17
Conference paper
Electronic Resource
English
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