Visual Detection of Portunus Survival Based on YOLOV5 and RCN Multi-Parameter Fusion

Abstract

Single-frame circulation aquaculture belongs to the important category of sustainable agriculture development. In light of the visual-detection problem related to survival rate of Portunus in single-frame three-dimensional aquaculture, a fusion recognition algorithm based on YOLOV5, RCN (RefineContourNet) image recognition of residual bait ratio, centroid moving distance, and rotation angle was put forward. Based on three-parameter identification and LWLR (Local Weighted Linear Regression), the survival rate model of each parameter of Portunus was established, respectively. Then, the softmax algorithm was used to obtain the classification and judgment fusion model of Portunus’ survival rate. In recognition of the YOLOV5 residual bait and Portunus centroid, the EIOU (Efficient IOU) loss function was used to improve the recognition accuracy of residual bait in target detection. In RCN, Portunus edge detection and recognition, the optimized binary cross-entropy loss function based on double thresholds successfully improved the edge clarity of the Portunus contour. The results showed that after optimization, the mAP (mean Average Precision) of YOLOV5 was improved, while the precision and mAP (threshold 0.5:0.95:0.05) of recognition between the residual bait and Portunus centroid were improved by 2% and 1.8%, respectively. The loss of the optimized RCN training set was reduced by 4%, and the rotation angle of Portunus was obtained using contour. The experiment shows that the recognition accuracy of the survival rate model was 0.920, 0.840, and 0.955 under the single parameters of centroid moving distance, residual bait ratio, and rotation angle, respectively; and the recognition accuracy of the survival rate model after multi-feature parameter fusion was 0.960. The accuracy of multi-parameter fusion was 5.5% higher than that of single-parameter (average accuracy). The fusion of multi-parameter relative to the single-parameter (average) accuracy was a higher percentage.