基于计算机视觉的垃圾塑料瓶识别与定位算法研究  

Research on recognition and location algorithm of waste plastic bottle based on computer vision

作　　者：曾维[1] 尹生阳 张凤[1] Zeng Wei;Yin Shengyang;Zhang Feng(School of Mechanical and Electrical Engineering,Chengdu University of Technology,Chengdu 610051,China)

机构地区：[1]成都理工大学机电工程学院,成都610051

出　　处：《电子测量技术》2021年第23期12-17,共6页Electronic Measurement Technology

基　　金：国家重点研发计划项目(2018YFC1505102)资助。

摘　　要：针对当前垃圾分拣算法对废旧塑料瓶检测效率低、环境受限和仅限于颜色识别等问题,提出了一种有效的垃圾塑料瓶识别与定位方法,以采集高像素图像为基础提取原始图片,通过YOLOv3算法的浅层增强特征将图片中目标进行一系列卷积获得不同的特征,并输入各检测分支进行检测,将不同尺度特征图经过K-means聚类算法做锚框处理,利用位置预测实现最终的识别与定位检测结果。通过模型测试,YOLOv3算法在识别速度和算法的繁琐性上都优于其他的一些算法,平均识别准确率达到90%、检测时间约0.4 s以内、定位精度约±5 cm。证明了此算法对于复杂环境下废旧塑料瓶目标检测的有效性和实用性。In view of the low detection efficiency of waste plastic bottles,limited environment and limited color recognition by current waste sorting algorithms,proposes an effective method for identifying and locating waste plastic bottles,which extracts original pictures based on high-pixel images.Through the shallow enhancement feature of the YOLOv3 algorithm,the target in the picture is subjected to a series of convolutions to obtain different features,and each detection branch is input for detection,and the feature maps of different scales are processed by the K-means clustering algorithm as anchor boxes,and the position is used predict to achieve the final recognition and location detection results.Through model testing,the YOLOv3 algorithm is superior to other algorithms in terms of recognition speed and complexity of the algorithm.The average recognition accuracy is 90%,the detection time is within 0.4 s,and the positioning accuracy is about ±5 cm.It proves the effectiveness and practicability of this algorithm for target detection of waste plastic bottles in complex environments.

关 键 词：塑料瓶分类 YOLOv3 K-MEANS聚类 识别速度 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]