An Analysis of Yolov5s-based Cocoon Categorization Detection
Sericulture is a traditional culture in China.The proportion of reliance on manual labor in the existing cocoon processing is still high.With the continuous development of artificial intelligence,the applica-tion level of segmentation,localization,and classification of silkworm cocoons based on deep learning is con-stantly improving.Aiming at the characteristics of the current cocoon selection technology with strong artificial laziness,a cocoon detection system based on deep learning is designed.Based on the Yolov5s model,effec-tive identification is carried out for the upper cocoon,double palace cocoon,yellow spot cocoon,and thin skin cocoon.Through cocoon model matching,the results of the originally set detection label priority are dis-played to solve the original equipment detection,which can only detect the disadvantages of one side.Can better reduce the manual selection of cocoon misjudgment,improve the detection of the correct rate,and in-crease detection efficiency.
万方数据
维普
