EGISD-YOLO: Edge Guidance Network for Infrared Ship Target Detection

In the marine field, infrared detection technology is of great significance for timely localization and detection of ships in security missions. However, since infrared ship targets are often in the environmental conditions of small pixel occupancy, low contrast and complex background, it poses a gr...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.10097-10107
Main Authors: Zhan, Weida, Zhang, Cong, Guo, Shufang, Guo, Jinxin, Shi, Mingkai
Format: Article
Language:English
Subjects:
Attention mechanism
Background noise
edge guidance
Environmental conditions
Feature extraction
Image edge detection
infrared target detection
Localization
Location awareness
Marine technology
Marine vehicles
Modules
Object detection
Semantics
ship image
Target detection
Task analysis
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Summary:In the marine field, infrared detection technology is of great significance for timely localization and detection of ships in security missions. However, since infrared ship targets are often in the environmental conditions of small pixel occupancy, low contrast and complex background, it poses a great challenge for multiship detection, classification, and localization tasks. Therefore, to address these problems we propose an edge information-guided infrared ship target detection (EGISD-YOLO) network, in which a dense-csp structure is designed to improve the csp module of YOLO to increase the reusability of the backbone feature information, and in addition, to address the noise and interference generated by the image in the complex background, a deconvolutional channel attention module (DCA) is designed to link the contextual language to the image, which relates the contextual semantics to obtain the local information of the target. Crucially, we propose an edge-guided structure that takes the edge information of low-level features as a cue to fuse with deep-level features to enrich the target contour and thus improve the target localization ability, so that the network still possesses robustness under low-contrast conditions, and finally, we add a small-size prediction head at the end of the network to further increase the detection ability of weak targets. The proposed EGISD-YOLO is experimentally demonstrated to have better detection performance for infrared ship targets.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3389958