DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM

YUAN Hongchun; ZANG Tianqi

doi:10.13205/j.hjgc.202307029

Volume 41 Issue 7

Jul. 2023

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YUAN Hongchun, ZANG Tianqi. DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 214-221. doi: 10.13205/j.hjgc.202307029

Citation:

YUAN Hongchun, ZANG Tianqi. DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 214-221. doi: 10.13205/j.hjgc.202307029

YUAN Hongchun, ZANG Tianqi. DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 214-221. doi: 10.13205/j.hjgc.202307029

Citation:

YUAN Hongchun, ZANG Tianqi. DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 214-221. doi: 10.13205/j.hjgc.202307029

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DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM

doi: 10.13205/j.hjgc.202307029

YUAN Hongchun,
ZANG Tianqi^,

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2022-05-15

Abstract

Abstract

Underwater trash detection technology is of great significance for automatic trash removal tasks by underwater robots. However, it faces some challenges, such as an unsatisfactory detection rate due to poor underwater light conditions and high computation load. To solve these problems, this paper proposed an improved YOLOv5 model for underwater trash detection. First, in the preprocessing stage, Gamma transform was introduced to improve the grey level and contrast of underwater images for model detection. Meanwhile, the CBAM attention mechanism was embedded in the detection part of the YOLOv5 model to select the information important to underwater trash detection tasks and suppress uncritical information, thus improving the accuracy of the algorithm. Besides, in the neck layer, the traditional convolution module was replaced by the Ghost convolution module to reduce the calculation amount and improve the detection speed. The proposed model was evaluated on an underwater trash dataset in the real environment. Compared with mainstream object detection algorithms, the proposed model achieved the highest detection accuracy of 93.7% in images with a resolution of 640×640, and the calculation time was only 6.7 ms, meeting the requirements of real-time performance. The study results provide a good reference for underwater trash detection.
- deep learning,
- underwater trash detection,
- attention mechanism,
- Gamma transformation,
- ghost convolution

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References(27)

References

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