Small object intelligent detection method based on adaptive recursive feature pyramid

As we all know, YOLOv4 can achieve excellent detection performance in object detection and has been effectively applied in many fields. However, the inconsistency of scale features affects the prediction accuracy of the path aggregation network (PANet) in YOLOv4 for small objects, resulting in low detection accuracy. This paper presents YOLOv4, which uses an adaptive recursive path aggregation network (AR-PANet) to improve the detection accuracy of small objects. First, the output characteristics of the PANet are fed back into the backbone network by using a recursive structure to enrich the characteristic information of the object. Second, an adaptive approach is developed to eliminate conflicting information in multi-scale feature space, thereby enhancing scale invariance and promoting feature extraction accuracy for small objects. Finally, the CBAM is used to map the multi-scale features obtained from the AR-PANet to independent channels and spatial dimensions to achieve feature refinement, thus improving the detection accuracy of small objects. Experimental results show that our proposed method can effectively improve the accuracy of small object detection in multiple datasets, addressing this challenging problem with impressive results. Thus, our proposed approach has great potential and valuable applications in the fields of remote sensing and intelligent transportation.