A Lightweight Detection Method for Meretrix Based on an Improved YOLOv8 Algorithm

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.