SAFDet: A Semi-Anchor-Free Detector for Effective Detection of Oriented Objects in Aerial Images

Remote Sensing

He Sun¹  Stephen Marshall¹  Junwei Han²  Huimin Zhao³  Jinchang Ren³  Zhenyu Fang³ 

[1] Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK;School of Automation, Northwestern Polytechnical University, Xi’an 710109, China;School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China;

关键词: rotate region;    convolutional neural network;    anchor free;    aerial object detection;   

DOI  :  10.3390/rs12193225

来源: DOAJ

【 摘 要 】

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned horizontal proposals and the interference from complex backgrounds. To tackle these issues, region of interest transformer and attention models were proposed, yet they are extremely computationally intensive. To this end, we propose a semi-anchor-free detector (SAFDet) for object detection in aerial images, where a rotation-anchor-free-branch (RAFB) is used to enhance the foreground features via precisely regressing the OBB. Meanwhile, a center-prediction-module (CPM) is introduced for enhancing object localization and suppressing the background noise. Both RAFB and CPM are deployed during training, avoiding increased computational cost of inference. By evaluating on DOTA and HRSC2016 datasets, the efficacy of our approach has been fully validated for a good balance between the accuracy and computational cost.

【 授权许可】

Unknown