YOLO-based mussel （Mytilus edulis） recognition and detection technology is the key to achieving mechanization and intelligence in the grading, seedling separation, and other operational links of mussels. However, the unclear and indefinite external features of mussels present a challenge to improving recognition accuracy. This paper proposes a mussel object detection model based on the improved YOLOv5 algorithm （CST-YOLO）, which integrates the CoordAttention attention mechanism to enhance feature expression ability, uses the SIoU boundary box regression Loss function to reduce the boundary box regression loss and improve the detection speed of the model, and designs an improved decoupled head TSCODE to enhance the detection accuracy. The algorithm testing was performed on a self-constructed dataset of mussels, and the experimental results demonstrated that the CST-YOLO algorithm exhibited an improvement in accuracy （P） by 0.428% compared to the YOLOv5 algorithm, and mAP （mean Average Precision） at IoU thresholds of 0.5 and 0.95 reached 92.221%, indicating a significant increase of 1.583%. These findings highlight the effective enhancement of mussel object detection accuracy while ensuring optimal detection speed achieved by the CST-YOLO algorithm. The present study contributes to the advancement of machine vision technology in facilitating the automation and intelligent production and processing within the mussel farming industry.