Underwater Target Recognition Based on Improved YOLOv4 Neural Network
The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreove...
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doaj-88c011a19e2041199d8ba030f8657bf82021-07-23T13:37:59ZengMDPI AGElectronics2079-92922021-07-01101634163410.3390/electronics10141634Underwater Target Recognition Based on Improved YOLOv4 Neural NetworkLingyu Chen0Meicheng Zheng1Shunqiang Duan2Weilin Luo3Ligang Yao4College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaThe YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well.https://www.mdpi.com/2079-9292/10/14/1634underwater imageenhancementYOLO neural networktarget recognitionmosaic data augmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lingyu Chen Meicheng Zheng Shunqiang Duan Weilin Luo Ligang Yao |
spellingShingle |
Lingyu Chen Meicheng Zheng Shunqiang Duan Weilin Luo Ligang Yao Underwater Target Recognition Based on Improved YOLOv4 Neural Network Electronics underwater image enhancement YOLO neural network target recognition mosaic data augmentation |
author_facet |
Lingyu Chen Meicheng Zheng Shunqiang Duan Weilin Luo Ligang Yao |
author_sort |
Lingyu Chen |
title |
Underwater Target Recognition Based on Improved YOLOv4 Neural Network |
title_short |
Underwater Target Recognition Based on Improved YOLOv4 Neural Network |
title_full |
Underwater Target Recognition Based on Improved YOLOv4 Neural Network |
title_fullStr |
Underwater Target Recognition Based on Improved YOLOv4 Neural Network |
title_full_unstemmed |
Underwater Target Recognition Based on Improved YOLOv4 Neural Network |
title_sort |
underwater target recognition based on improved yolov4 neural network |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-07-01 |
description |
The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well. |
topic |
underwater image enhancement YOLO neural network target recognition mosaic data augmentation |
url |
https://www.mdpi.com/2079-9292/10/14/1634 |
work_keys_str_mv |
AT lingyuchen underwatertargetrecognitionbasedonimprovedyolov4neuralnetwork AT meichengzheng underwatertargetrecognitionbasedonimprovedyolov4neuralnetwork AT shunqiangduan underwatertargetrecognitionbasedonimprovedyolov4neuralnetwork AT weilinluo underwatertargetrecognitionbasedonimprovedyolov4neuralnetwork AT ligangyao underwatertargetrecognitionbasedonimprovedyolov4neuralnetwork |
_version_ |
1721288687093809152 |