基于深度学习优化YOLOV3算法的芳纶带检测算法研究  

Research on aramid band detection algorithm based on deep learning optimization YOLOV3 algorithm

作　　者：杨建伟[1] 涂兴子[1] 梅峰漳 李亚宁 范鑫杰 YANG Jianwei;TU Xingzi;MEI Fengzhang;LI Yaning;FAN Xinjie(Pingdingshan Tianan Coal Industry Co.,Ltd.,Pingdingshan 467000,China;Shanxi Dedicated Measurement Control Co.,Ltd.,Taiyuan 030000,China)

机构地区：[1]平顶山天安煤业股份有限公司煤矿机电运输,河南平顶山467000 [2]山西戴德测控技术有限公司研发部,山西太原030000

出　　处：《中国矿业》2020年第4期67-72,共6页China Mining Magazine

摘　　要：矿用芳纶带传送设备在长期运输过程中会产生划伤、砸伤等损伤。芳纶带表面缺陷需要及时的检测,而传统机器视觉检测精度低、受背景干扰比较大、漏检率和误检率较高,因此,本文提出运用深度学习神经网络检测,查看一次统一的实时对象检测(you only look once unified real-time object detection,YOLO)。在现场的测试中,YOLOV3算法对小目标的识别精度比较低,敏感度不够,本文优化了YOLOV3算法,网络信息的传输过程,由ResNet(残差网络)替换为特征表述更为完整的DenseNet(密集连接网络),同时运用了卷积降维进行优化,减少检测时间。在现场经过比对,优化后的YOLOV3算法相较于通过频域变换和Otsu算法,检测精度提高了26%,对比没有优化的YOLOV3算法,检测精度提高了15%,通过在现场的实验,该方法有效地改善了对于芳纶带小目标的瑕疵检测。In the long-term transportation process,the mining aramid belt conveyance equipment will produce scratch,smash and other damages.The surface defects of aramid strip should detect timely,but the traditional machine vision detection has some problems such as low accuracy,large background interference,high rate of missing detection and false detection.You only look once unified real-time object detection(YOLO)is proposed to use deep learning neural network detection.In the field test,YOLOV3 neural network has a low identification accuracy and insufficient sensitivity to small targets.In this paper,YOLOV3 neural network is optimized,and the transmission process of network information is replaced by DenseNet(dense connection network)with more complete feature expression from the previous ResNet(residual network).Meanwhile,convolution dimension reduction is applied for optimization to reduce detection time.After comparison in the field,the optimized YOLOV3 algorithm improved the detection accuracy by 26%compared with the previous one through frequency domain transformation and Otsu algorithm.Compared with the non-optimized YOLOV3 algorithm,the detection accuracy is increased by 15%.Through field experiments,this method could effectively improve the detection of defects on the small target of aryl ribbon.

关 键 词：表面缺陷 YOLOV3算法 密集连接网络(DenseNet) 卷积降维 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]