Closely arranged inshore ship detection using a bi-directional attention feature pyramid network

Guo, Hao; Gu, Dongbing

doi:10.1080/01431161.2023.2277166

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…This enables cross-layer multi-scale weighted feature fusion, and a channel attention mechanism is introduced in the convolutional units to further enhance the detection effectiveness of small ship targets in infrared images. Guo et al [12] proposed a nearshore ship detection method based on the FCOS network, incorporating a bidirectional attention feature pyramid network. This approach enhances the detection accuracy of small targets.…”

Section: Introductionmentioning

confidence: 99%

GT-YOLO: Nearshore Infrared Ship Detection Based on Infrared Images

Wang,

Huo

et al. 2024

JMSE

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Traditional visible light target detection is usually applied in scenes with good visibility, while the advantage of infrared target detection is that it can detect targets at nighttime and in harsh weather, thus being able to be applied to ship detection in complex sea conditions all day long. However, in coastal areas where the density of ships is high and there is a significant difference in target scale, this can lead to missed detection of some dense and small targets. To address this issue, this paper proposes an improved detection model based on YOLOv5s. Firstly, this article designs a feature fusion module based on a fusion attention mechanism to enhance the feature fusion of the network and introduces SPD-Conv to improve the detection accuracy of small targets and low-resolution images. Secondly, by introducing Soft-NMS, the detection accuracy is improved while also addressing the issue of missed detections in dense occlusion situations. Finally, the improved algorithm in this article increased mAP0.5 by 1%, mAP0.75 by 5.7%, and mAP0.5:0.95 by 5% on the infrared ship dataset. A large number of comparative experiments have shown that the improved algorithm in this article is effective at improving detection capabilities.

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Section: Introductionmentioning

confidence: 99%