Research on underwater biological detection method based on multi-scale fusion and attention mechanism
Authors: 
Longyu Tang, Yunong Yang, Hong WangAuthors Info & Claims
Longyu Tang, Yunong Yang, Hong WangAuthors Info & ClaimsPages 821 - 826
Abstract
Underwater robots are an important tool for exploiting marine resources, while underwater target detection is a key technology for marine fishing by underwater robots. For the problem that the traditional algorithm is difficult to detect small targets and multiple scales in the underwater environment, this paper proposes an underwater biological detection algorithm based on the multi-scale feature fusion and attention mechanism. First, a YOLOv5 network is constructed, and a bi-directionally weighted BiFPN feature pyramid structure is fed into the neck network so that multiscale target features can be efficiently merged. Second, a channel and spatial convolutional attention mechanism is introduced between the neck network and the prediction network in order to increase the saliency of the underwater target that needs to be detected, which effectively reduces the missed and erroneous detection of underwater targets. The experimental results show that the proposed algorithm can accurately detect multi-scale underwater targets in complex underwater environments with an average accuracy of 80.18%, which is 2.87% better than YOLOv5, and an average recall of 85.06%, which is 4.81% better than YOLOv5.
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Published In
December 2022
1396 pages
ISBN:9781450398343
DOI:10.1145/3584376
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 April 2023
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RICAI 2022
RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence
December 16 - 18, 2022
Dongguan, China
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Overall Acceptance Rate 140 of 294 submissions, 48%
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