LCE-Net: Local-Aware and Context Enhancement based YOLOv5 for object detection in remote sensing images

Remote sensing image target detection has been a research hotspot in the field of remote sensing. Aiming at the problems of complex background of remote sensing images, few pixels and large scale variability of remote sensing targets, a Local-Aware and Context Enhancement network(LCE-Net) is propose...

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Bibliographic Details
Published in2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) pp. 107 - 115
Main Authors Yang, Xinxiu, Cui, Zhiqiang, Wang, Feng, Xu, Liming, Feng, Zhengyong
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.10.2022
Subjects
context
Feature extraction
local-aware
Location awareness
Machine learning algorithms
Object detection
Prediction algorithms
remote sensing images
Semantics
Transformers
YOLOv5
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Summary:Remote sensing image target detection has been a research hotspot in the field of remote sensing. Aiming at the problems of complex background of remote sensing images, few pixels and large scale variability of remote sensing targets, a Local-Aware and Context Enhancement network(LCE-Net) is proposed with YOLOv5m as the baseline model. Firstly, the context enhancement module is designed in the network extraction layer to increase the perceptual field to fully extract feature information. Secondly, a cascade Swin Transformer block is added at the detection to capture feature information of object in similar environments. Thirdly, Alpha-CIoU to improve the localization accuracy. We validate the remote sensing image target detection algorithm on the DOTA dataset and the Plane dataset. The experimental results show that our algorithm increases the overall mAP from 69.4% to 73% compared to the YOLOv5m algorithm, which improves the remote sensing image target detection performance.
DOI:10.1109/ICICML57342.2022.10009829