基于深度学习的X射线鞋底异物检测系统  

X-Ray Detection System for Foreign Bodies in Sole of Shoes Based on Deep Learning

作　　者：肖力炀 李伟[1] 高荣 申浩 王孟 XIAO Li-Yang;LI Wei;GAO Rong;SHEN Hao;WANG Meng(School of Information Engineering,Chang’an University,Xi’an 710064,China)

机构地区：[1]长安大学信息工程学院,西安710064

出　　处：《计算机系统应用》2021年第3期88-94,共7页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(51978071)。

摘　　要：在服饰鞋厂的加工生产过程中经常会出现断针现象,残留在鞋子里的多余断针等金属异物会威胁人们的人身安全.针对这一问题,本文提出了一种基于深度学习的鞋底金属异物检测系统.首先,将鞋子依次放在传送带上送入检针机,经过X光照射采集图像.之后对采集到的图像进行预处理操作,使金属异物变得清晰.最后通过深度学习网络模型识别当前图像是否含有金属异物,并检测异物所处位置.实验结果表明,经过图像预处理和微调标注框的做法,能有效提高模型识别的精度.本文提出模型的平均精度为97.6%,该结果表明此模型可以有效检测遗留在各种鞋类中不同形状的金属异物,具有很好的商业价值.Broken needles are frequently seen in the production process of clothing and shoe factories. This study proposes a detection system of metal foreign bodies in sole of shoes based on deep learning since those residual bodies such as broken needles in shoes will threaten people’s safety. Firstly, shoes are put on a conveyor belt in turn and sent to a needle detector, and the images are collected by X-ray irradiation. After that, the images are preprocessed to highlight the small metal foreign bodies. Finally, metal foreign bodies and their positions are detected with a deep learning network model. Experimental results show that preprocessing images and fine-tuning the label box can make metal foreign bodies clearer, and the average precision of the model is 97.6%. It proves that the model can effectively detect the metal foreign bodies with different shapes left in footwear, presenting great commercial potential.

关 键 词：深度学习 Faster R-CNN 断针检测 异物检测 

分 类 号：O434.1[机械工程—光学工程] TP18[理学—光学] TP391.41[理学—物理] TS943.79[自动化与计算机技术—控制理论与控制工程]