Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

• Published in: Wireless communications and mobile computing Vol. 2021; no. 1
• Main Authors: Qin, Leow Wei, Ahmad, Muneer, Ali, Ihsan, Mumtaz, Rafia, Zaidi, Syed Mohammad Hassan, Alshamrani, Sultan S., Raza, Muhammad Ahsan, Tahir, Muhammad
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2021; Hindawi Limited
• Subjects: Algorithms; Applications programs; Classification; Consumption; Datasets; Decision trees; Deep learning; Economic activity; Edge computing; Electronic devices; Image classification; Impact analysis; Industry 4.0; Lightweight; Machine learning; Mobile computing; Model accuracy; Municipal solid waste; Municipal waste management; Neural networks; Recycling; Sensors; Solid waste management; Standard of living; Support vector machines
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2021/9963999

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.