Year: 2024
Type: article
Abstract: Accurate and effective identification of fish species is crucial for the processing and transportation of fish products. It enables efficient tracking and management of fish products, making it an essential aspect of the industry. Traditional methods of fish identification usually require expert knowledge within a particular domain. However, these methods can struggle to capture complex fish characteristics, particularly under varying lighting and angle conditions. In the article, an effective recognition algorithm named YOLO-FD is proposed. First, a novel feature extraction module has replaced the C2f module in YOLOv8, which effectively reduces the amount of model parameter calculations. The Efficient Multi-Scale Attention (EMA) applied in this study adeptly preserves spatial and channel information, fostering inter-regional interaction and enhancing feature extraction within the backbone network. The loss function in YOLOv8 was improved to address the sample imbalance problem. The YOLO-FD as an effective fish recognition algorithm addresses challenges faced by traditional methods simultaneously enhancing identification accuracy and offering lightweight improvement.
Source: Computer Fraud & Security
DOI: 10.52710/cfs.93
Author: Aimin Lu
Institution: Guangdong Ocean University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Guangdong Ocean University
Awards: G2582550001, G6445246018
OpenAlex ID: W4407001522