CB-YOLOv5: Streetlight Detection Based on Low-Light Images in High-Interference Environment

The monitoring and operation maintenance (O&M) of urban streetlights is crucial for traffic safety and socio-economic development. However, how to accurately and robustly detect streetlights in low-light and high-interference environments is still a problem that concerns researchers. In recent years, deep learning has made remarkable progress in the field of object detection, among which the single-stage detection algorithm represented by You Only Look Once (YOLO) shows a satisfactory detection effect. It brings a new opportunity to detect streetlights based on images collected in a complicated street environment. Therefore, this study proposes an improved YOLOv5 model, as CB-YOLOv5, to accurately and robustly detect streetlights based on low-light images with high interferences. This proposed model integrates a Convolutional Block Attention Module (CBAM) and Bidirectional Feature Pyramid Network (BiFPN) to enhance its learning ability of spatial and channel dimension feature information, promote information fusion and transfer between multi-scale objects. Experimental results show that compared with the standard YOLOv5 algorithm, the proposed CB-YOLOv5 model can achieve significant improvement in accuracy and ability of interference-resistant in streetlight detection tasks. The mAP0.5 reached 0.968, which is 23.5% higher than that of the standard YOLOv5 algorithm. In general, the CB-YOLOv5 model provides a new method to detect small objects in low-light and complex scenes. The developed method is also expected to provide a theoretical basis for automated monitoring and operation maintenance of urban lighting facilities.