Research on small and medium target detection algorithm for unmanned aerial vehicles based on faster R-CNN

Drone is widely applied to human production life with the development of drone technology. Therefore, it is very advantageous to carry out research on target detection in UAV images. Using target detection technology, achieve accurate capture of the target, thereby achieving accurate positioning of the target, and achieving the goal of meeting various mission requirements. Unmanned aerial vehicle (UAV) images have the characteristics of large changes in target scale, small overall target size, and are limited by viewing angle, which pose a great challenge to the detection of UAV targets. This thesis mainly focuses on the research of target detection in UAV images. This article proposes an optimization method for small object detection based on Faster R-CNN technology to address the problems of low accuracy in small object detection and missed detection of multi-scale targets in existing drone image object detection algorithms. The improved feature pyramid network structure FPN adopts top-down, bottom-up, and horizontal connection structures. Reduce prediction box bias caused by pooling regions of interest through feature pooling using bilinear interpolation. Finally, the improved algorithm was experimentally validated on the MSCOCO dataset. The faster and Faster R-CNN small object detection algorithm proposed in this article improves the average accuracy of the optimized algorithm from 91.16% to 96.13%. The Faster R-CNN algorithm has improved by 4.97% compared to the original one. The speed of the improved algorithm has been increased to 37FPS, which is approximately doubled.