
Research on Efficient Color Recognition Method for Waste Plastic Bottles Based on Deep Learning
XIE Shi-long, WU Hu, MAO Wen-jie, CHU Xian-long, YANG Xian-hai
Research on Efficient Color Recognition Method for Waste Plastic Bottles Based on Deep Learning
To address the value difference between different colors in the recycling process of waste plastic bottles, the study provides an efficient color recognition approach for waste plastic bottles based on a deep learning-upgraded YOLOv8n model to handle the problem of color recognition and sorting. Adding a Bidirectional Feature Pyramid Network (BiFPN) to the neck network for multi-scale feature fusion to improve the model's generalizability. The decoupled head structure of the head network uses only one Conv2d module for both branches, and a reparameterized convolution RepConv module is added at the front end of the branch to reduce computational complexity and improve training accuracy. Replace the CIOU loss function with the WIOU v3 loss function to improve the model's detection accuracy. Comparative trials demonstrate that the model suggested in the paper is superior to typical object detection models. The results showed that the model in the article had a 44.8% lower parameter count, a 34.6% lower computational complexity, and a 2.7% higher mean average precision at 50% IOU (mAP50) than the original model. When identifying the colors of waste plastic bottles, the frames per second (FPS) can reach 66, which is 40.4% faster than the original model. The colors of waste plastic bottles may now be detected in real time and with high accuracy.
YOLOv8n / Waste plastic bottles / Classification recognition / Object detection
1 |
李晔,许文.中国塑料制品市场分析与发展趋势[J].化学工业,2021,39(4):37-43.
|
2 |
薛志宏,刘鹏,高叶玲.废旧塑料回收与再利用现状研究[J].塑料科技,2021,49(4):107-110.
|
3 |
高珊.中国绿色包装材料研究现状与进展[J].内蒙古科技与经济,2018(17):3,6.
|
4 |
张文华,原心红,刘金妹,等.废旧塑料在道路工程建设中的应用[J].塑料科技,2022,50(2):93-97.
|
5 |
赵爱之.废弃塑料回收方法概述[J].塑料科技,2020,48(9):123-126.
|
6 |
张悦.塑料垃圾资源化处理探析[J].清洗世界,2023,39(10):178-180.
|
7 |
杨朝义,李海强,黄芬梅.计算机视觉技术在塑料成品检测中的应用[J].塑料科技,2021,49(5):99-102.
|
8 |
邢晶凯,刘腾腾,王波.可闭环回收塑料的研究进展[J].中外能源,2023,28(9):92-100.
|
9 |
李洪波,廖详刚,陈立.基于机器学习One-stage目标检测算法的塑料自动识别系统[J].塑料科技,2020,48(12):86-89.
|
10 |
周晓,焦晨,朱开瑄.基于卷积神经网络的废旧塑料瓶颜色分拣系统[J].数字制造科学,2021,19(3):227-232.
|
11 |
曾维,尹生阳,张凤.基于计算机视觉的垃圾塑料瓶识别与定位算法研究[J].电子测量技术,2021,44(23):12-17.
|
12 |
王振,方海峰,曹晋,等.基于YOLOv5s的轻量化可回收饮料瓶颜色识别[J].国外电子测量技术,2023,42(3):160-166.
|
13 |
|
14 |
|
15 |
|
16 |
赵永强,饶元,董世鹏,等.深度学习目标检测方法综述[J].中国图象图形学报,2020,25(4):629-654.
|
17 |
许德刚,王露,李凡.深度学习的典型目标检测算法研究综述[J].计算机工程与应用,2021,57(8):10-25.
|
18 |
|
19 |
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
|
25 |
|
26 |
|
27 |
|
28 |
侯学良,单腾飞,薛靖国.深度学习的目标检测典型算法及其应用现状分析[J].国外电子测量技术,2022,41(6):165-174.
|
29 |
|
30 |
|
31 |
|
32 |
齐志,史旭龙,刘昊.一种无计算增量但提高精度的RepConv通用卷积模块及使用策略:CN114819073A[P].2022-07-29.
|
33 |
董恒祥,潘江如,董芙楠,等.基于YOLOv5s模型的边界框回归损失函数研究[J].现代电子技术,2024,47(3):179-186.
|
/
〈 |
|
〉 |