A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline
This work addresses the challenge of automatic marine organism detection for aquaculture and ecological monitoring, but it is incremental as it builds on existing YOLOv5 and attention mechanisms.
The paper tackled the problem of low-quality underwater images hindering marine organism detection by proposing a pipeline that integrates a convolutional block attention module into YOLOv5 and a self-adaptive global histogram stretching algorithm, achieving improved detection performance on the URPC2021 benchmark dataset.
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.