An underwater target recognition method based on improved YOLOv4 in complex marine environment

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Jili Zhou, Q. Yang, Huijuan Meng, D. Gao
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引用次数: 6

Abstract

In the marine environment, there are problems such as complex background and low illumination, resulting in poor picture quality, and the aggregation of small targets and multiple targets brings difficulties to target recognition. In order to improve the accuracy of marine target detection, the image enhancement and improved YOLOv4 algorithm are used to identify marine organisms. Firstly, aiming at the problems of image blur, some of the images are enhanced with the multi-scale retinex with colour restoration (MSRCR) enhancement algorithm so that the image is clearer and it is easier to extract features. Secondly, Mosaic augmentation is added to YOLOv4 to enrich the data set and increase network robustness. Then the Spatial Pyramid Pooling (SPP) module of YOLOv4 is improved by changing the size of the pooling core, which increases the range of feature extraction and improves the detection capabilities, and its mAP value reaches 97.06%. The experimental results show that the detection accuracy of the image enhancement and improved YOLOv4 algorithm is 7.16% higher than that of the original algorithm. And the improved YOLOv4 algorithm is improved in average precision and recall rate compared with other algorithms, which verifies the effectiveness of the algorithm.
基于改进YOLOv4的复杂海洋环境下水下目标识别方法
在海洋环境中,存在背景复杂、照度低等问题,导致图像质量差,小目标和多目标的聚集给目标识别带来困难。为了提高海洋目标检测的精度,采用图像增强和改进的YOLOv4算法对海洋生物进行识别。首先,针对图像模糊的问题,对部分图像采用多尺度retinex with colour restoration (MSRCR)增强算法进行增强,使图像更清晰,更容易提取特征;其次,在YOLOv4中加入了马赛克增强,丰富了数据集,提高了网络的鲁棒性。然后通过改变池化核的大小对YOLOv4的空间金字塔池化(SPP)模块进行改进,增加了特征提取的范围,提高了检测能力,其mAP值达到97.06%。实验结果表明,图像增强和改进后的YOLOv4算法的检测精度比原算法提高了7.16%。与其他算法相比,改进后的YOLOv4算法在平均精度和召回率方面都有提高,验证了算法的有效性。
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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