Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms

塑料污染 分类 计算机科学 塑料废料 环境科学 环境污染 人工智能 工艺工程 生化工程 微塑料 废物管理 工程类 算法 化学 环境化学 环境保护
作者
Jang-Hee Choi,B. D. Lim,Youngjun Yoo
出处
期刊:Applied sciences [Multidisciplinary Digital Publishing Institute]
卷期号:13 (18): 10224-10224 被引量:39
标识
DOI:10.3390/app131810224
摘要

Plastics, with their versatility and cost-effectiveness, have become indispensable materials across various industries. However, the improper disposal and mismanagement of plastic waste have led to significant environmental issues, including pollution, habitat destruction, and threats to wildlife. To address these challenges, numerous methods for plastic waste sorting and recycling have been developed. While conventional techniques like near-infrared spectroscopy (NIRS) have been effective to some extent, they face difficulties in accurately classifying chemically similar samples, such as polyethylene terephthalate (PET) and PET-glycol (PET-G), which have similar chemical compositions but distinct physical characteristics. This paper introduces an approach that adapts image sensors and deep learning object detection algorithms; specifically, the You Only Look Once (YOLO) model, to enhance plastic waste classification based on the shape of the waste. Unlike conventional methods that rely solely on spectral analysis, our methodology aims to significantly improve the accuracy and efficiency of classifying plastics, especially when dealing with materials having similar chemical compositions but differing physical attributes. The system developed using image sensors and the YOLO model proves to be not only effective but also scalable and adaptable for various industrial and environmental applications. In our experiments, the results are strikingly effective. We achieved a classification accuracy rate exceeding 91.7% mean Average Precision (mAP) in distinguishing between PET and PET-G, surpassing conventional techniques by a considerable margin. The implications of this research extend far and wide. By enhancing the accuracy of plastic waste sorting and reducing misclassification rates, we can significantly boost recycling efficiency. The proposed approach contributes to a more sustainable and efficient plastic waste management system, alleviating the strain on landfills and mitigating the environmental impact of plastic waste, contributing to a cleaner and more sustainable environment.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Orange应助yishu采纳,获得10
1秒前
科研通AI6.3应助nn采纳,获得10
2秒前
4秒前
4秒前
桑什么桑发布了新的文献求助20
4秒前
友好老师发布了新的文献求助10
4秒前
6秒前
7秒前
jingjuan发布了新的文献求助10
9秒前
10秒前
HH完成签到,获得积分10
10秒前
超级盼海完成签到,获得积分10
11秒前
程皮皮发布了新的文献求助10
11秒前
虚拟的清炎完成签到 ,获得积分10
12秒前
14秒前
15秒前
害羞的XM完成签到,获得积分10
15秒前
16秒前
禹平露发布了新的文献求助30
16秒前
哈哈哈哈哈哈完成签到 ,获得积分10
17秒前
gndz发布了新的文献求助10
17秒前
nav发布了新的文献求助10
18秒前
Lulululuying发布了新的文献求助10
18秒前
黄晃晃发布了新的文献求助10
18秒前
19秒前
内向汉堡发布了新的文献求助10
20秒前
科研通AI6.4应助jingjuan采纳,获得10
21秒前
晨曦发布了新的文献求助10
21秒前
高高的远山完成签到,获得积分10
22秒前
欢_211完成签到,获得积分10
22秒前
bkagyin应助Cclaaa采纳,获得10
22秒前
球球完成签到,获得积分10
22秒前
23秒前
24秒前
星光熠熠完成签到 ,获得积分10
27秒前
juju发布了新的文献求助10
27秒前
31秒前
易如反掌发布了新的文献求助10
32秒前
科研通AI6.4应助晨曦采纳,获得10
32秒前
羽翮发布了新的文献求助10
34秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 2500
Atlas of Aligner Treatment and Planning A Case-Based Approach 1000
Geist der Kunst und Kultur 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Soil mites of the family Rhagidiidae (Actinedida: Eupodoidea). Morphology, Systematics, Ecology 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7422996
求助须知:如何正确求助?哪些是违规求助? 9026070
关于积分的说明 19228709
捐赠科研通 7052596
什么是DOI,文献DOI怎么找? 3235358
关于科研通互助平台的介绍 2398385
邀请新用户注册赠送积分活动 2217709