Traffic Multi-target Detection Method Based on Improved YOLOv7-Tiny
In the complex multi-object traffic environment ,there are challenges such as diverse detection categories ,intricate background information ,and ineffective detection due to low image resolution. Commonly used object detection algorithms fail to achieve high-precision real-time detection. To address these issues ,we propose an improved algorithm for multi-object traffic detection based on YOLOv7-Tiny.In the enhanced algorithm ,we first employ Partial Convolution (PConv) to replace the original convolution ,optimize model parameters and runtime speed. Next ,we integrate the lightweight operator , Context-Aware Reassembly Feature (CARAFE),to replace the previous nearest-neighbor interpolation in the upsampling section ,enhancing feature fusion capabilities. Lastly ,Efficient Classification Loss (EfficiCLoss) is introduced to replace the original loss function ,improving the localization accuracy of bounding boxes and mitigating the issue of missed detections caused by occlusion. Additionally,we create a multi-object dataset based on complex traffic scenarios for experimentation. The results demonstrate that the enhanced detection algorithm achieves a 4.3％ improvement in mean Average Precision (mAP) compared to the original YOLOv7-Tiny network. Furthermore ,the detection speed increases by 12.5％,and the parameter count decreases by 30％,meeting the requirements for real-time detection in intelligent transportation systems.
万方数据
维普
