Real-time tilapia fillet defect segmentation on edge device for robotic trimming

Abstract

The implementation of robotic tilapia fillet trimming instead of manual labor is a pivotal advancement in intelligent fish processing, offering substantial superiority in operational efficiency and product quality. The study presents an improved model called TFDS-YOLOv8n for tilapia fillet defects segmentation. The model incorporates Coordinate Attention (CA) into the feature extraction layer, thereby enhancing its ability to capture characteristics at various levels of the input. Additionally, the feature fusion layer is reconstructed as Slim-Neck to reduce the number of parameters without compromising prediction accuracy. Furthermore, the bounding box loss function is modified by MPDIoU to expedite the model convergence. The proposed model was tested employing the dataset collected from the practical tilapia processing plant. Ablation experiments demonstrate that TFDS-YOLOv8n achieves a reduction of 0.29 MB in parameters and 1 G in Floating Point Operations (FLOPs) while increasing bbox_mAP and mask_mAP by 2.8 % and 2.5 %, respectively. Eventually, the model is further accelerated by TensorRT and deployed on the edge device. Experimental results show that the quantized model possesses a faster inference speed than the untransformed model, which realizes real-time detection of tilapia fillet defects and facilitates robotization in fish processing.