A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images

作者：Liming Zhou, Zhehao Liu, Hang Zhao, Yane Hou, Yang Liu, Xianyu Zuo, Lanxue Dang · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15143468 · 被引用次数：26 · 研究领域：Advanced Neural Network Applications、Advanced Image and Video Retrieval Techniques、Robotics and Sensor-Based Localization

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Feature Fusion Module (FFM) is proposed, which can better fuse features by learning the importance of different scale features and improve the representation ability of multi-scale features by reusing feature maps to help models better detect multi-scale objects. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, we tested the CGMDet on VisDrone and UAVDT datasets. The proposed CGMDet improves mAP0.5 by 1.9% on the VisDrone dataset and 3.0% on the UAVDT dataset.