Two-stage small target detection algorithm based on feature fusion

To solve the problem that the traditional general algorithm can not detect small targets well, this paper adds a new feature fusion branch and introduces an attention mechanism on the basis of yolov4 algorithm, which avoids feature aliasing caused by simple fusion and enhances the expression ability of the network for small targets. Compared to the original network, mAP improved by 2.2%. On this basis, considering that the low confidence prediction box also contains the prediction information of small targets, but the traditional method directly rejects it rudely, resulting in missing detection. In this paper, the K-means clustering algorithm is used to analyze the size distribution characteristics of the targets in the data set, then the image is divided into regions, the region containing the low confidence prediction frame is enlarged by super resolution, refined detection, the results are mapped back to the original location, and the results are uniformly processed by NMS. In the end, compared with the original network, mAP improves by 4.4%, and the network's recall rate of small targets has been significantly improved by nearly 8%, which can effectively improve the performance of small target detection.