基于YOLO v3的卷烟用瓦楞纸箱表面缺陷检测方法

Surface Defect Detection Method for Corrugated Box for Cigarettes Based on YOLO v3 Algorithm

提出了一种基于YOLO v3的检测算法,并建立了卷烟用瓦楞纸箱循环利用性能评价体系。对比了Faster RCNN与YOLO v3深度神经网络目标检测算法对采集的典型卷烟用瓦楞纸箱表面缺陷的识别结果。基于OpenCV库、Canny算法,开发了适用于测量卷烟用瓦楞纸箱表面缺陷尺寸及其分布位置的合理方案。结果表明,该算法的平均准确率达92.23%,可成功实现缺陷位置、尺寸的检测。

A detection algorithm based on YOLO v3 was proposed,and an evaluation system for the recycling performance of corrugated box for cigarettes was established.The recognition results of surface defects of collected typical corrugated box for cigarettes using Faster RCNN and YOLO v3 deep neural network target detection algorithms were compared.Based on the OpenCV library and Canny algorithm,a reason⁃able solution suitable for measuring the sizes and distribution locations of surface defects of corrugated box for cigarettes was developed.The results showed that the algorithm had an average accuracy of 92.23%and could successfully achieve the detection of defect locations and size.