Anchor-Free Small Object Detection Algorithm Based on Multi-scale Feature Fusion

In order to overcome the problems of the small proportion of the object, the variable shooting angle, and height in the UAV aerial image, an anchor-free small object detection algorithm based on the YOLOX network is proposed in this paper. Firstly, aiming at the problem of small object size, a Tri-Head module is proposed to change the size of the detection head to obtain more original information on small objects. Secondly, in order to accurately detect dense continuous small objects in complex scenes, a CBAM-CSP module incorporating the CBAM attention mechanism is proposed to increase the weights for detecting dense continuous regions of interest in images. Then, to alleviate the problems caused by background clutter and pixel blur, BiNet, a jumping multi-scale feature enhancement module, is proposed. This module fully integrates shallow feature information and deep feature information so that detection heads of different scales can obtain enough semantic image information and spatial information. Finally, the experimental results on the VisDrone dataset show that, comparing with the YOLOX network, the mAP is increased by 7.56%±0.06% when the model parameters are reduced by about 5MB, which proves that the proposed algorithm can effectively improve the detection accuracy of small targets.