CLAM MEAT DETECTION ALGORITHM BASED ON IMPROVED YOLOv5s

Intelligent and accurate shelling technology is essential for improving the quality of clam products. To enable the rapid and precise localization of clam meat in Ruditapes philippinarum (with half-shell) on an automated processing line, an improved clam meat detection algorithm - EET-YOLOv5, based on YOLOv5s - is proposed. This algorithm enables real-time detection and localization of clam meat on the production line. It integrates the Efficient Local Attention (ELA) mechanism to enhance target localization, adopts the EIoU loss function to reduce bounding box regression error, and replaces the original detection head with a TSCODE decoupled head to improve detection accuracy. The algorithm achieved a Precision of 93.03%, Recall of 97.03%, and mean Average Precision (mAP) of 93.55%, with a detection speed of 13.3 ms. Compared to YOLOv4, Faster R-CNN, SSD, and the standard YOLOv5 series, EET-YOLOv5 demonstrated superior performance. It was deployed on a test workbench for positioning experiments, achieving an average response time of 1.8 seconds and a positioning success rate of 92.7%, indicating its suitability for automated clam shell-meat separation production lines.