The research of small target detection is of great significance for the optimization and improvement of tasks such as small face recognition and remote sensing image detection. However

the small target in the image occupies fewer pixels

lower resolution

unobvious feature information

resulting in the effect that existing methods for small target detection is not ideal. To solve this problem

a feedback-based feature fusion network (ReFPN) for YOLOV4 algorithm was proposed.The original feature layer extracted from the backbone network is used twice to enhance the feature information of small targets and position regression performs more accurately. At the same time

the compound attention mechanism (Co-AM) was proposed to more fully extract the detail feature information of small targets

suppress invalid features

and further improve small targets’ detection accuracy.Experimental results show that the method improves the AP of YOLOV4 algorithm on MS COCO dataset by 1.9%

and the APS by 3.3%. The effectiveness of our method is proved

and the detection effect of small target detection is better than the existing algorithms.