A multi-scale network-based approach for optical imagery ship detections

Subjects: Traffic and Transportation Engineering >> Ship Engineering submitted time 2024-03-23

WU Yuanyuan ZANG Bohan WANG Xinghua LIU Shuai ZHANG Zongliang

Abstract： In recent years, there has been an increasing demand for higher detection and classification accuracy of ship targets to enable safe ship navigation, driving the development of ship intelligence. However, the performance of deep learning-based ship target detection algorithms is affected by the optical imaging process of ship targets, which can be easily disrupted by environmental factors such as wind, current, rain, and fog. Additionally, the diverse range of ship types, morphologies, and sizes pose challenges for accurate detection and identification of ship targets. To address these challenges, this paper proposes a multi-scale neural network-based target detection method for improving the accuracy of ship target detection in optical images. The proposed method employs a Convolutional Neural Networks (CNN) to extract image features. The improved backbone of CSPDarkNet and multi-scale network is used to realize the accurate detection of the ship-borne optical camera on the water ship target, and the detection accuracy of the model for small targets and dense targets is improved. Furthermore, label smoothing to prevent overfitting, and non-maximum suppression to reduce repetitive detections. Experimental results demonstrate that the proposed model achieves accurate detection of ship targets on water and can be used for the detection of small and intensive targets. The mean average precision (mAP) of the proposed method on the Ship-Detection dataset reaches 84.80, which outperforms previous research methods such as Faster-RCNN, DINO and offers greater potential for practical applications.

Peer Review Status:Awaiting Review

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