基于YOLO的手机外观缺陷视觉检测算法  

A visual detection algorithm for mobile phone appearance defect based on YOLO

作　　者：杨戈[1,2] 刘思瀚 Yang Ge;Liu Sihan(Key Laboratory of Intelligent Multimedia Technology,Beijing Normal University,Zhuhai 519087,China;Advanced Institute of Natural Sciences,Beijing Normal University,Zhuhai 519087,China)

机构地区：[1]北京师范大学珠海分校智能多媒体技术重点实验室,广东珠海519087 [2]北京师范大学自然科学高等研究院,广东珠海519087

出　　处：《电子技术应用》2022年第7期81-85,共5页Application of Electronic Technique

基　　金：广东高校省级重大科研项目(2019KZDXM015,2020ZDZX3058);广东省学科建设专项资金(2013WYXM0122);智能多媒体技术重点实验室(201762005);校级教学团队(202012);校级课程思政(201932);2020年广东省教改项目(655);2021年广东省课程教研室(104)。

摘　　要：针对在小规模手机屏幕缺陷数据检测效率不高的问题,提出了一个基于YOLO v3的手机外观缺陷视觉检测算法,即YOLO-q-v3算法。YOLO-q-v3算法通过改进DarkNet-53网络结构,减少原网络的层数和相应的模型参数,有效地提高算法的检测效率。将YOLO v3算法和本文提出的YOLO-q-v3算法对同一数据集进行对比实验,实验结果表明,YOLO-q-v3算法能够正确识别手机屏幕裂痕,YOLO-q-v3在检测速率上比YOLO v3算法提高了24%。Aiming at the problem that some algorithms are not efficient in the detection of small-scale mobile phone screen defect data,this paper proposes a visual detection algorithm for mobile phone appearance defect based on YOLO v3,namely YOLO-q-v3 algorithm.The YOLO-q-v3 algorithm improves the DarkNet-53 network structure,reduces the number of layers of the original network and the corresponding model parameters,and effectively improves the detection efficiency of the algorithm.The YOLO v3 algorithm and the YOLO-q-v3 algorithm proposed in this paper are compared and tested on the same data set.The experimental results show that the YOLO-q-v3 algorithm can correctly identify the cracks on the mobile phone screen,and the detection rate of YOLO-q-v3 is 24%higher than that of the YOLO v3 algorithm.

关 键 词：YOLO 屏幕缺陷检测 深度学习 

分 类 号：TN183[电子电信—物理电子学]