Paper Info
Title
Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images
Abstract
Oil storage tank detection and classification in synthetic aperture radar (SAR) images play a vital role in monitoring energy distribution and consumption. Due to the SAR side-looking imaging geometry and multibouncing scattering mechanism, dense oil tank detection and classification tasks have faced more challenges, such as overlapping, blurred contours, and geometric distortion, especially for small-sized tanks. To address the above issues, this paper proposes YOLOX-TR, an improved YOLOX based on the Transformer encoder and structural reparameterized VGG-like (RepVGG) blocks, to achieve end-to-end oil tank detection and classification in densely arranged areas of large-scale SAR images. Based on YOLOX, the Transformer encoder, a self-attention-based architecture, is integrated to enhance the representation of feature maps and capture the region of interest of oil tanks in densely distributed scenarios. Furthermore, RepVGG blocks are employed to reparameterize the backbone with multibranch typologies to strengthen the distinguishable feature extraction of multi-scale oil tanks without increasing computation in inference time. Eventually, comprehensive experiments based on a Gaofen-3 1 m oil tank dataset (OTD) demonstrated the effectiveness of the Transformer encoder and RepVGG blocks, as well as the performance superiority of YOLOX-TR with a mAP and mAP(0.5) of 60.8% and 94.8%, respectively.
Year
DOI
Venue
2022
10.3390/rs14143246
REMOTE SENSING
Keywords
DocType
Volume
YOLOX-TR, oil tank classification, large-scale SAR images, dense oil tanks, Transformer encoder, RepVGG
Journal
14
Issue
ISSN
Citations 
14
2072-4292
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Qian Wu100.34
Bo Zhang2419.80
Changgui Xu300.68
Hong Zhang4719126.06
Chao Wang5895190.04