Combining YOLO and background subtraction for small dynamic target detection

YOLO, an important algorithm for target detection, is ineffective in detecting small dynamic targets. In this paper, we utilize background subtraction, which is highly sensitive to dynamic pixels, to provide YOLO with the location and features of small dynamic targets, thus reducing the missed detection rate of small targets. This method uses background subtraction and YOLO to obtain the mask and class of the target, respectively. If the target's mask and class can be detected, the features of YOLO and Masks data module are constructed or updated using its characteristics and class. Conversely, if only the target mask is obtained, the target mask is introduced into the features of YOLO and Masks data module for similarity detection, so as to determine the target class. Finally, YOLO performs the forced detection of the target based on the coordinates of the mask with the determined class. Validated with the SBMnet dataset, the experimental results show that for dynamic targets with three different line-of-sight distances, the method proposed in this paper improves the precision by 2.3%, recall by 3.5%, and F1-score by 3.1%.