YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds
Xiao Zhou, Lang Jiang, Hu CaiXia, Shuai Lei, Tingting Zhang, Xingang Mou
Abstract
To improve the detection ability of infrared small targets in complex backgrounds, an improved detection algorithm YOLO-SASE is proposed in this paper. The algorithm is based on the YOLO detection framework and SRGAN network, taking super-resolution reconstructed images as input, combined with the SASE module, SPP module, and multi-level receptive field structure while adjusting the number of detection output layers through exploring feature weight to improve feature utilization efficiency. Compared with the original model, the accuracy and recall rate of the algorithm proposed in this paper were improved by 2% and 3%, respectively, in the experiment, and the stability of the results was significantly improved in the training process.