Research on Candy Bubble Defect Recognition Based on YOLOv7
During the manufacturing process of hard candies,problems such as unstable syrup temperatures and une-ven stirring can result in the formation of air bubble defects in the candies,which can negatively impact their appear-ance and taste.Therefore,it is necessary to detect and remove hard candies that contain air bubbles.To tackle the issue of small and randomly positioned air bubble defects in candies,this paper introduces a candy air bubble defect recognition algorithm based on YOLOv7.Firstly,colored images and depth images of the candies are collected.His-togram equalization and median filtering are then applied to the colored images in order to establish a database of candy air bubble defects.A YOLOv7-based model for recognizing air bubble defects is constructed,incorporating Si-mOTA and ReOrg operations to achieve a balance between processing speed and recognition accuracy.Finally,the con-structed model for recognizing candy air bubble defects is compared with existing deep learning object detection algo-rithms.Experimental results demonstrate that the proposed recognition model exhibits the best overall performance.
NETL
NSTL
万方数据
维普
