UAV IMAGE OBJECT DETECTION BASED ON ATTENTION MECHANISM AND DILATED CONVOLUTION

The existing UAV image object detection algorithms suffer the problems of low detection accuracy on small objects and missed detection of multi-scale objects. A UAV image object detection algorithm based on channel attention mechanism and parallel structure dilated convolution feature fusion is proposed to overcome these problems. In order to improve the feature expression ability of the algorithm from channel and receptive field, we redesigned the ResNet50 via adding the SENet and PSDCFFN to the backbone. We used ROI Align and redesigned the RPN anchor size via K-Means to reduce the coordinate deviation in the object regression process. The experimental results show that the proposed algorithm can improve the accuracy of object detection in UAV images. On RSOD-Dataset and UAV image data sets, the mAP of the proposed algorithm reaches 92.52% and 98.07% separately.