Small object intelligent Detection method based on Adaptive Cascading Context

Abstract

With the technology advances, deep learning-based object detection has made unprecedented progress. However, the small spatial ratio of object pixels affects the effective extraction of deep details features, resulting in poor detection results in small object detection. To improve the accuracy of small object detection, an adaptive Cascading Context small (ACC) object detection method is proposed based on YOLOv5. Firstly, a separate shallow layer feature was proposed to obtain more detailed information beneficial to small object detection. Secondly, an adaptive cascade method is proposed to fuse the output features of the three layers of the pyramid to adaptively filter negative semantic information, while fusing with shallow features to solve the problem of low classification accuracy caused by insufficient semantic information of shallow features. Finally, an adaptive context model is proposed to use a deformable convolution to obtain spatial context features of shallow small objects, associating the targets with the background, thereby improving the accuracy of small object detection. The experimental results show that the detection accuracy of the proposed method has been improved by 6.12%, 3.35%, 3.33%, and 5.2%, respectively, compared with the source code on the PASCAL VOC, NWPU VHR-10, KITTI, and RSOD datasets, which fully demonstrate the effectiveness of our method in small object detection.