A method of single‐shot target detection with multi‐scale feature fusion and feature enhancement

Abstract

Abstract The Single Shot MultiBox Detector (SSD) is one of the fastest detection algorithms. Although it has achieved good results in detection, it also has the problem of poor detection effect for small targets and occlusion between objects. Here, the authors propose a new target detection method called single‐shot target detection with multi‐scale feature fusion and feature enhancement. Here, the authors introduce multi‐scale feature fusion module, feature enhancement module and efficient channel attention module, and integrate them into the detection module of the original SSD target detection algorithm to improve the ability of network feature extraction. Experimental results on pascal VOC 2007 datasets show that the proposed algorithm works well when the input size is 300 × 300, the detection speed reaches 41.7 frames per second (FPS) and the detection accuracy reaches 79.6%, which is 2.4% higher than the original SSD target detection algorithm. When the input size is 512 × 512, the detection accuracy is 81.9%, and the detection speed reaches 36.5 FPS, which is 3.2% higher than the original SSD target detection algorithm. According to the experimental results, our algorithm has a better performance when there are many objects in the image and there is occlusion.