Research on Infrared Small Target Detection Algorithm Based on Improved YOLOv8

Aiming at the problem of the high error rate of infrared small-target recognition and the large loss of model regression in complex backgrounds, an improved YOLOv8_SG (Small goals) algorithm was proposed by adding a small target detection layer and introducing the SA attention mechanism and WIoU_v3 loss function, which can fuse deeper features and have a larger receptive field. Moreover, the influence of the uneven labeling quality of the training samples was reduced, the position accuracy of the prediction box was improved, and the ability to detect small targets was enhanced. The experimental results show that the mAP of the improved algorithm increased from 0.8514 to 0.8997, and the overall loss effect of Box_loss increased by 34.9%. The proposed algorithm has a higher feature extraction ability and higher detection accuracy for small-target detection.