Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7

Abstract

Due to the impact of scale variation of vehicle targets and changes in traffic environments in large-scale traffic monitoring systems, vehicle target detection methods often face challenges. To improve the adaptability of detection methods to these variations, we proposed an enhanced YOLOv7 for traffic systems (ETS-YOLOv7). To mitigate the effects of complex environments, we introduced the convolutional block attention module (CBAM) into the YOLOv7 framework, which filters important features in both channel and spatial dimensions, thereby enhancing the model’s capability to recognize traffic object features. To address the influence of aspect ratio variations in vehicle targets, we replaced the original complete intersection over union (CIoU) with wise intersection over union v3 (WIoUv3), eliminating the aspect ratio consistency loss and enhancing the model’s ability to generalize and its overall performance. Additionally, we employed the compact layer aggregation networks (CLAN) module to replace the efficient layer aggregation networks (ELAN) module, reducing redundant computations and improving computational efficiency without compromising model accuracy. The proposed method was validated on the large-scale traffic monitoring dataset UA-DETARC, achieving a mean average precision (mAP0.5–0.95) of 90.2%, which is a 3% improvement over the original YOLOv7. The frames per second (FPS) reached 149, demonstrating that the proposed model is highly competitive in terms of detection efficiency and vehicle detection accuracy compared to other advanced object detection methods.

Keywords

• traffic detection
• surveillance scenarios
• attention mechanism
• roadside perception