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Infrared remote sensing ship image object detection model based on YOLO In multiple environments

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Abstract

Infrared object detection constitutes a significant ship-targeting methodology, exerting a vital role in maritime safety. The contemporary research regarding infrared ship imagery is insufficient and remains in need of addressing the issues related to smaller object sizes and more elaborate information. To overcome these challenges, we introduce RD-YOLO, a model dedicated to ship object detection in remote sensing, with a focus on infrared images. RD-YOLO incorporates a Receptive Field Convolution module, which fully exploits the receptive field features to enhance the global feature perception capacity of RD-YOLO. Furthermore, it utilizes a multi-scale network, namely the Deep Convergence Network (DCNnet), to improve the fusion of remote sensing information within RD-YOLO. The DCNnet introduces two innovative modules to boost target detection in infrared remote sensing images. The Multiscale Fusion Module resolves the challenge of omitting detailed information from larger-scale features and ensures comprehensive characterization. The Spatial Feature Module integrates multi-scale features through input compression with 3D convolution, augmenting the network's ability to capture indistinct texture information. The experimental results demonstrate that the mAP50 of RD-YOLO on the SFISD open dataset attains 94.53%, which is 4.22% higher than that of YOLOv8s; the mAP50 of RD-YOLO on the ship remote sensing dataset of Shandong University reaches 98.91%, which is 1.87% higher than that of YOLOv8s, thereby validating the high efficiency of this method. It is exceptionally suitable for infrared ship target detection in various environments.

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No datasets were generated or analysed during the current study.

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Funding

This work was supported by the Heilongjiang Province Provincial Higher Education Institutions Basic Research Operating Expenses Program under Grant (2022-KYYWF-0569).

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Conceptualization, Yilin Ge and Haowen Ji; methodology, Yilin Ge , Haowen Ji, and Xingli Liu; software, Haowen Ji; validation, Haowen Ji; formal analysis, Yinlin Ge and Haowen Ji; investigation, Haowen Ji; resources, Haowen Ji; data curation, Haowen Ji; writing—original draft preparation, Yilin Ge; writing—review and editing, Haowen Ji. All the authors have read and agreed to the published version of the manuscript.

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Correspondence to Haowen Ji.

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Ge, Y., Ji, H. & Liu, X. Infrared remote sensing ship image object detection model based on YOLO In multiple environments. SIViP 19, 85 (2025). https://doi.org/10.1007/s11760-024-03656-6

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