Year: 2025
Type: article
Abstract: Clam farms are typically located in remote areas with limited computational resources, making it challenging to deploy traditional deep learning-based object detection methods due to their large model size and high computational demands. To address this issue, this paper proposes a lightweight detection method, YOLOv8-RFD, based on an improved YOLOv8 algorithm, tailored for clam sorting applications. The proposed enhancements include the following: replacing the original backbone network of YOLOv8 with a Reversible Columnar Network (RevColNet) to reduce feature redundancy and computational load; upgrading the C2f modules in both the backbone and neck networks to C2f-Faster to optimize feature fusion strategies and improve fusion efficiency; and incorporating a Dynamic Head (DyHead) to enhance feature extraction and detection accuracy by adaptively adjusting the detection head structure. Experimental results on a custom clam dataset demonstrate that, compared to the original YOLOv8 model, the proposed method reduces the number of parameters by 22.75% and computational demand by 18.52%, while slightly improving detection accuracy. These improvements not only maintain but also enhance detection performance, significantly reducing computational cost, and confirming the method’s suitability for deployment in resource-constrained environments. This provides a reliable technical foundation for the sorting of clams.
Source: Applied Sciences
DOI: 10.3390/app15126647
Authors: Zhongxu Tian, Sifan Hou, Xiaoxue Yue, Xuewen Hu
Institution: Shanghai Ocean University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G1681795158
OpenAlex ID: W4411286038