基于YOLO神经网络的垃圾检测与分类

doi:10.16180/j.cnki.issn1007-7820.2022.10.008

Abstract

Abstract:

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%.

Key words: garbage recognition, classification, neural network, attentional mechanism, deep learning, data set, feature integration, object detection

CLC Number:

TP183

ZHANG Wei,LIU Na,JIANG Yang,LI Qingdu. Garbage Detection and Classification Based on YOLO Neural Network[J].Electronic Science and Technology, 2022, 35(10): 45-50.

Figures/Tables 11

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References 18

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垃圾种类	YOLOv4	Proposed model
Cans	90.21%	94.56%
Cigarette	89.62%	90.85%
CigaretteCase	89.67%	89.12%
FruitPeel	83.52%	86.27%
Package	91.37%	93.81%
Paper	81.64%	85.24%
PlasticBottle	87.41%	93.73%
SeedSheel	76.52%	77.39%
Leaf	84.63%	87.24%

backbone	卷积层	感受野	参数
CSPResNEXt50	16	425×425	20.6 MB
CSPDarknet53	29	725×725	27.6 MB

网络模型	K-means	Attention	map@0.5
YOLOv4	×	×	85.26%
YOLOv4-1	√	×	86.34%
Proposed model	√	√	88.07%

Garbage Detection and Classification Based on YOLO Neural Network

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