Lightweight ship target detection algorithm based on improved YOLOv5 algorithm model

Abstract

Abstract: Environmental perception equipment based on visual sensors plays a critical role in enabling autonomous navigation of liquefied natural gas （LNG） vessels. Due to the complex sea environment and the diversity of ships,the existing ship target detection algorithm can hardly meet the requirements of both high accuracy and real-time detection. To address the above problems,a lightweight ship target detection algorithm is proposed. Based on the YOLOv5 algorithm model,the MobileNetv3 structure is used to replace the backbone of YOLOv5,which significantly reduces the number of parameters in the backbone network and the amount of network computation. The Convolutional Block Attention Module （CBAM） is also added at the end of the feature fusion network to enhance the weight of useful features and optimize the accuracy of target detection. The initial anchor for ship target detection is obtained by K-means++ clustering algorithm,and the model is trained and evaluated by using the self-built dataset. The experimental results show that the improved YOLOv5 MC model has a mAP of 95.66%,which is 2.75% and 4.7% higher than YOLOv5 and YOLOv5 M models,respectively. The number of parameters of the model is reduced by 81.5% compared with the YOLOv5 model,which is about 45.40 MB. FPS reaches 39.55 frames per second,meeting the requirements of LNG vessels for real-time detection,and achieving good results in detecting small and occluded targets.

Key words: liquefied natural gas （LNG）, ship target detection, YOLOv5, MobileNetv3, Convolutional Block Attention Module （CBAM）

CLC Number:

U675.3

ZHANG Bo. Lightweight ship target detection algorithm based on improved YOLOv5 algorithm model[J]. TIANJIN SCIENCE & TECHNOLOGY, 2025, 52(04): 64-69.

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