Object detection algorithm based on fusion of spatial information

Abstract

Aiming at the problem that the small target size is small and the feature extraction is difficult, which leads to the poor detection effect of small target, this paper proposes a target detection algorithm based on the fusion of spatial information. Firstly, this paper takes the single-stage target detection algorithm yolov5 as the basic model, and adds a spatial information detection network module to solve the problem that the features of small targets are gradually reduced or completely lost in down-sample, so as to retain more spatial information in the low-level network. Secondly, in the feature fusion part, multiscale feature fusion module is used to fuse high-level semantic information and low-level spatial information, so as to locate small targets more accurately. Finally, the fusion features are used for the detection task to improve the accuracy of small target detection. The experimental results show that the Mean Average Precision (mAP) value detected on the COCO test set reaches 42%, which proves the effectiveness of the algorithm for small target detection.