Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

Meena Malik, Chander Prabha, Punit Soni, Varsha Arya, Wadee Alhalabi Alhalabi, Brij B. Gupta, Aiiad A. Albeshri and Ammar Almomani
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Meena Malik: Department of CSE, Chandigarh University, Mohali, India
Chander Prabha: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
Punit Soni: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
Varsha Arya: Department of Business Administration, Asia University, Taiwan, & Chandigarh University, Chandigarh, India
Wadee Alhalabi Alhalabi: Department of Computer Science, Immersive Virtual Reality Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
Brij B. Gupta: Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & School of Computing, Skyline University College, Sharjah, UAE & Lebanese American University, Beirut, Lebanon & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India
Aiiad A. Albeshri: Faculty of Computing and Information Technology, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia
Ammar Almomani: School of Computing, Skyline University College, Sharjah, UAE & Al- Balqa Applied University, Jordan

International Journal on Semantic Web and Information Systems (IJSWIS), 2023, vol. 19, issue 1, 1-20

Abstract: Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

Date: 2023
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