YOLOv5和RGB近红外融合的岸上塑料垃圾检测:实现准确高效环境监测的最新解决方案

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data and Cognitive Computing Pub Date : 2023-05-29 DOI:10.3390/bdcc7020103
Owen Tamin, E. Moung, J. Dargham, Farashazillah Yahya, A. Farzamnia, F. Sia, Nur Faraha Mohd Naim, L. Angeline
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引用次数: 0

摘要

塑料垃圾是一个日益严重的环境问题,对陆上生态系统、人类健康和野生动物构成了重大威胁。据估计,海洋中塑料垃圾的累积量每年超过800万吨,给海洋生物和食物链带来了危险。塑料垃圾在城市地区普遍存在,给可能摄入或卷入其中的动物带来风险,并对经济和旅游业产生负面影响。有效的塑料废物管理需要一种全面的方法,包括减少消费、促进回收和开发创新技术,如自动塑料检测系统。因此,开发准确高效的塑料检测方法对于有效的废物管理至关重要。为了应对这一挑战,YOLOv5模型等机器学习技术已成为开发自动塑料检测系统的有前途的工具。此外,由于塑料垃圾在不同环境中的独特特性,有必要将可见光(RGB)和近红外(RGNIR)作为塑料垃圾检测的一部分进行研究。为此,利用包括RGB和RGNIR图像的两个塑料垃圾数据集来训练所提出的模型YOLOv5m。然后在两个数据集上使用10倍交叉验证方法评估模型的性能。通过在训练数据集中添加背景图像来减少误报,对实验进行了扩展。进行了额外的实验来融合RGB和RGNIR数据集。提出了一种称为加权度量分数(WMS)的性能度量分数,其中WMS等于0.5的交集(IoU)阈值的平均精度之和(mAP@0.5)×0.1,并且在0.5到0.95的不同IoU阈值上平均的平均精度(mAP@0.5:0.95)×0.9。此外,还实施了10倍交叉验证程序。基于这些结果,当在测试数据集上进行评估时,所提出的模型使用RGB和RGNIR数据集的融合实现了最佳性能,平均值为mAP@0.5,mAP@0.5:0.95,WMS分别为92.96%±2.63%、69.47%±3.11%和71.82%±3.04%。这些发现表明,在机器学习中利用正常可见光和近红外光谱作为特征表示,可以提高塑料垃圾检测的性能。这为开发用于自动化、环境管理和资源管理等领域的自动塑料检测系统开辟了新的机会。
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On-Shore Plastic Waste Detection with YOLOv5 and RGB-Near-Infrared Fusion: A State-of-the-Art Solution for Accurate and Efficient Environmental Monitoring
Plastic waste is a growing environmental concern that poses a significant threat to onshore ecosystems, human health, and wildlife. The accumulation of plastic waste in oceans has reached a staggering estimate of over eight million tons annually, leading to hazardous outcomes in marine life and the food chain. Plastic waste is prevalent in urban areas, posing risks to animals that may ingest it or become entangled in it, and negatively impacting the economy and tourism industry. Effective plastic waste management requires a comprehensive approach that includes reducing consumption, promoting recycling, and developing innovative technologies such as automated plastic detection systems. The development of accurate and efficient plastic detection methods is therefore essential for effective waste management. To address this challenge, machine learning techniques such as the YOLOv5 model have emerged as promising tools for developing automated plastic detection systems. Furthermore, there is a need to study both visible light (RGB) and near-infrared (RGNIR) as part of plastic waste detection due to the unique properties of plastic waste in different environmental settings. To this end, two plastic waste datasets, comprising RGB and RGNIR images, were utilized to train the proposed model, YOLOv5m. The performance of the model was then evaluated using a 10-fold cross-validation method on both datasets. The experiment was extended by adding background images into the training dataset to reduce false positives. An additional experiment was carried out to fuse both the RGB and RGNIR datasets. A performance-metric score called the Weighted Metric Score (WMS) was proposed, where the WMS equaled the sum of the mean average precision at the intersection over union (IoU) threshold of 0.5 (mAP@0.5) × 0.1 and the mean average precision averaged over different IoU thresholds ranging from 0.5 to 0.95 (mAP@0.5:0.95) × 0.9. In addition, a 10-fold cross-validation procedure was implemented. Based on the results, the proposed model achieved the best performance using the fusion of the RGB and RGNIR datasets when evaluated on the testing dataset with a mean of mAP@0.5, mAP@0.5:0.95, and a WMS of 92.96% ± 2.63%, 69.47% ± 3.11%, and 71.82% ± 3.04%, respectively. These findings indicate that utilizing both normal visible light and the near-infrared spectrum as feature representations in machine learning could lead to improved performance in plastic waste detection. This opens new opportunities in the development of automated plastic detection systems for use in fields such as automation, environmental management, and resource management.
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Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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