A Novel Underwater Image Enhancement and Improved Underwater Biological
Detection Pipeline
- URL: http://arxiv.org/abs/2205.10199v1
- Date: Fri, 20 May 2022 14:18:17 GMT
- Title: A Novel Underwater Image Enhancement and Improved Underwater Biological
Detection Pipeline
- Authors: Zheng Liu, Yaoming Zhuang, Pengrun Jia, Chengdong Wu, Hongli Xu ang
Zhanlin Liu
- Abstract summary: This paper proposes a novel method for capturing feature information, which adds the convolutional block attention module (CBAM) to the YOLOv5 backbone.
The interference of underwater creature characteristics on object characteristics is decreased, and the output of the backbone network to object information is enhanced.
- Score: 8.326477369707122
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: For aquaculture resource evaluation and ecological environment monitoring,
automatic detection and identification of marine organisms is critical.
However, due to the low quality of underwater images and the characteristics of
underwater biological, a lack of abundant features may impede traditional
hand-designed feature extraction approaches or CNN-based object detection
algorithms, particularly in complex underwater environment. Therefore, the goal
of this paper is to perform object detection in the underwater environment.
This paper proposed a novel method for capturing feature information, which
adds the convolutional block attention module (CBAM) to the YOLOv5 backbone.
The interference of underwater creature characteristics on object
characteristics is decreased, and the output of the backbone network to object
information is enhanced. In addition, the self-adaptive global histogram
stretching algorithm (SAGHS) is designed to eliminate the degradation problems
such as low contrast and color loss caused by underwater environmental
information to better restore image quality. Extensive experiments and
comprehensive evaluation on the URPC2021 benchmark dataset demonstrate the
effectiveness and adaptivity of our methods. Beyond that, this paper conducts
an exhaustive analysis of the role of training data on performance.
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