EBSE-YOLO: High Precision Recognition Algorithm for Small Target Foreign Object Detection

For the complex background along the electrified railroad, there are problems such as missed detection and false detection of invasive small target foreign objects. A high-precision recognition algorithm for small target foreign object detection is proposed. A channel attention mechanism ECA-Net is added to the backbone network of YOLOv5s so that the network can focus more on small target objects; borrowing from the network structure of weighted bidirectional feature pyramid network (BiFPN), the backbone network is fused with Neck by adding an edge to achieve cross-level feature fusion; the SPD-Conv module is used to replace the seventh layer convolutional kernel to increase the detail information extraction; using EIOU loss function to shorten the actual difference in width and height between the a priori frame and the real frame, The experimental results show that the improved algorithm increases the mAP value by 3.35 percentage points with little growth in computational volume, and the highest mAP value reaches 97.96%. The detection accuracy is greatly improved with a small loss of time cost, and the demand for small target foreign body detection in complex environments is achieved.