基于YOLO神经网络的垃圾检测与分类  

Garbage Detection and Classification Based on YOLO Neural Network

作　　者：张伟[1] 刘娜 江洋 李清都 ZHANG Wei;LIU Na;JIANG Yang;LI Qingdu(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Chongqing Internet Information Office,Chongqing 401120,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093 [2]上海理工大学医疗器械与食品学院,上海200093 [3]重庆市互联网信息办公室,重庆401120

出　　处：《电子科技》2022年第10期45-50,共6页Electronic Science and Technology

基　　金：国家自然科学基金(61773083);上海市浦江人才计划(2019PJD035)。

摘　　要：针对人工分拣垃圾效率低、任务重和环境恶劣等问题,文中提出了基于YOLO的目标检测方法来实现垃圾检测与分类。通过制作特定数据集,使用K-means聚类算法以及Mish激活函数对模型进行调整。根据卷积神经网络的特性,通过在YOLO模型的每个检测头前嵌入CBAM注意力模块,结合PANet增强特征集成能力来提升小目标检测的精度。实验结果表明,文中提出的垃圾检测与分类方法能够准确快速地识别垃圾。相较于YOLOv4,文中所提模型在垃圾数据集上的map值提升了2.81%,其中Cans的识别精度可达94.56%,PlasticBottle的精度提升了6.36%。In view of the problems of low efficiency of manual garbage sorting,heavy tasks and harsh environment,this study proposes a YOLO-based target detection method to realize garbage detection and classification.The model is adjusted through making a specific dataset,using K-means clustering algorithm and Mish activation function.According to the characteristics of the convolutional neural network,the CBAM attention module is embedded in front of each detection head of the YOLO model,combined with PANet to enhance the feature integration ability to improve the accuracy of small target detection.The experimental results show that the garbage detection and classification method proposed in this study can accurately and quickly identify garbage.Compared with YOLOv4,the map value of the proposed model on the garbage data set has increased by 2.81%.The recognition accuracy of Cans can reach 94.56%,and the accuracy of PlasticBottle has increased by 6.36%.

关 键 词：垃圾识别 分类 神经网络 注意力机制 深度学习 数据集 特征集成 目标检测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]