YOLOv5s-Based Lightweight Object Recognition with Deep and Shallow Feature Fusion

In object detection, targets in adverse and complex scenes often have limited information and pose challenges for feature extraction. To address this, we designed a lightweight feature extraction network based on the Convolutional Block Attention Module (CBAM) and multi-scale information fusion. Within the YOLOv5s backbone, we construct deep feature maps, integrate CBAM, and fuse high-resolution shallow features with deep features. We also add new output heads with distinct feature extraction structures for classification and localization, significantly enhancing detection performance, especially under strong light, nighttime, and rainy conditions. Experimental results show superior detection performance in complex scenes, particularly for pedestrian crossing detection in adverse weather and low-light conditions. Using an open-source dataset from Shanghai Jiao Tong University, our algorithm improves pedestrian crossing-detection precision (AP0.5:0.95) by 5.9%, reaching 82.3%, while maintaining a detection speed of 44.8 FPS, meeting real-time detection requirements. The source code is available at GitHub.