An Investigation of Infrared Small Target Detection by Using the SPT-YOLO Technique

To detect and recognize small size and submerged complex background target in infrared images, we combine a dynamic receptive field fusion strategy and a multi-scale feature fusion mechanism to improve the detection performance of small targets significantly. The space-to-depth convolution module is introduced as a downsampling layer in the backbone firstly, and achieved the same sampling effect. More detailed information retained at the same time. Thus the model's detection capability for small targets has been enhanced. Then the pyramid level 2 feature map with minimum receptive field and maximum resolution is added to the neck, which reduces the loss of positional information during feature sampled. Furthermore, x-small detection head are added, The understanding of the overall characteristics and structure of the target is enhanced much more, and the representation and localization of small targets has been improved. Finally, the cross-entropy loss function in the original network model is replaced by an adaptive threshold focal loss function, forcing the model to allocate more attention to target features. Above methods base on a public tool, the 8th version of You Only Look Once (YOLO) improved, it is named SPT-YOLO in this paper. Some experiments on datasets such as infrared small object detection (IR-SOT) and infrared small target detection 1K (IRSTD-1K), etc, have been executed to verify the proposed algorithm; and the mean average precision of 94.0% and 69% under condition of threshold at 0.5 and over a range from 0.5 to 0.95 is obtained, respectively. The results show that the proposed method achieves the best performance of infrared small target detection compared to existing methods.