A multimodule collaborative optimization method for YOLOv11-based vehicle detection in complex traffic scenes
To address the problems of weak small target features, target occlusion, severe weather interference, and bounding box regression bias in vehicle detection under complex traffic scenarios, a multi-scale feature fusion vehicle detection method is proposed. An improved YOLOv11 model is used for vehicle target detection. First, data augmentation techniques are used to expand the sample size for complex weather and lighting distortion scenarios to improve the model's environmental adaptability. Second, an efficient multi-scale attention mechanism (EMA) is embedded in the backbone network to enhance target feature extraction and suppress background redundancy. Third, a neck network using PSConv replaces the traditional downsampling convolution to optimize small target feature preservation and multiscale receptive field configuration (MSRF). Finally, an Inner-EIoU loss function is introduced during the training phase to improve bounding box regression accuracy and training stability. Experimental results show that on the UA-DETRAC benchmark dataset and the self-built complex traffic scene dataset, the mAP50 and mAP50-90 values of the vehicle detection model in complex scenes are 64.8% and 46.5%, respectively, which are improved by 4.5% and 4.2% by YOLOv11. From the perspective of computational efficiency, the improved model has a GFLOPs of 6.60, which is lower than YOLOv11's 6.90, and has better detection performance.