Double-sided Quality Detection System of Debark Peony Root Based on Machine Vision
A double-sided quality inspection system for debark peony root based on machine vision technology is proposed to improve the accuracy and efficiency of quality testing.Utilizing the principle of mirror reflection,images of both sides of the medicinal materials are simultaneously captured and processed to detect their quality.By comparing the recognition performance of single-sided and dual-sided images,the YOLOv8s model introduces the Shuffle Attention mechanism and Focal EIOU Loss optimization algorithm.Experimental results show that the average recognition accuracy for single-sided debark peony root is 94.4%,while for the dual-sided,it is 92.8%.With the improved algorithm,the average recognition accu-racy for dual-sided debark peony root images reaches 99.3%,while also mitigating prediction bias caused by sample imbalance.The experimental results verify the feasibility and practicality of the dual-sided quality inspection system for debark peony root medicinal materials.
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