Zhou, L.; Liu, Z.; Zhao, H.; Hou, Y.-E.; Liu, Y.; Zuo, X.; Dang, L. A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images. Remote Sens.2023, 15, 3468.
Zhou, L.; Liu, Z.; Zhao, H.; Hou, Y.-E.; Liu, Y.; Zuo, X.; Dang, L. A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images. Remote Sens. 2023, 15, 3468.
Zhou, L.; Liu, Z.; Zhao, H.; Hou, Y.-E.; Liu, Y.; Zuo, X.; Dang, L. A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images. Remote Sens.2023, 15, 3468.
Zhou, L.; Liu, Z.; Zhao, H.; Hou, Y.-E.; Liu, Y.; Zuo, X.; Dang, L. A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images. Remote Sens. 2023, 15, 3468.
Abstract
Unmanned Aerial Vehicles (UAVs) image object detection has great application value in 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 Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. 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, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.
Keywords
UAV images; multi-feature fusion; information aggregation; multi-scale object detection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
