基于改进 YOLOv3 算法的水下小目标分类与识别

doi:10.12066/j.issn.1007-2861.2279

Abstract

Abstract:

This study proposes an improved YOLOv3 algorithm designed to address the twin issues of low detection and recognition rate and high false alarm rate with respect to the detection of small targets by sonar. The improved YOLOv3 network is optimised on the basis of the original YOLOv3 algorithm, with the hierarchical connection of the network changed and the features of the shallow and deep layers fused to form a new larger-scale detection layer. Concurrently, the linear scaling $K$-means clustering algorithm is used to optimise the calculation of the number of a priori boxes and the aspect ratio, thereby improving the correlation between the a priori and ground truth boxes. These modifications improve the average accuracy of the YOLOv3 algorithm by 7%. Experimental results show that the proposed improvements to the YOLOv3 algorithm result in the effective identification of small targets with higher accuracy and lower false alarm rate, while maintaining the real-time processing capabilities of the YOLOv3 algorithm.

Key words: YOLOv3, small target detection, deep learning

CLC Number:

TB567

SHAO Huixiang, ZENG Dan. Classification and recognition of underwater small targets based on improved YOLOv3 algorithm[J]. Journal of Shanghai University（Natural Science Edition）, 2021, 27(3): 481-491.

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References 17

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Classification and recognition of underwater small targets based on improved YOLOv3 algorithm

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