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Solid Waste Management Scenario in India and Illegal Dump Detection Using Deep Learning: An AI Approach towards the Sustainable Waste Management

The study is presented in four sections. The first section defines the municipal solid waste and solid waste management system. The second section illustrates the descriptive statistical analysis of waste generation patterns in India. The average waste generation in India was 160,038.9 tons per day in 2021; 95% of this total waste was collected and transported to the disposal sites. Based on scientific studies and observations, the per capita waste generation rate in 2018 was 0.490–0.626 g per day. In the last one and a half decades (1999–2000 to 2015–2016), Delhi and Bangalore have shown the highest percentage growth of 2075% and 1750%, respectively, in total waste generation among the highest population cities. The analysis of waste generation patterns concludes urbanization is a major factor that highly influences the waste generation rate. The third section describes the major issues in current solid waste management services. Some of these issues are the unavailability of web portals for citizens, no real-time monitoring of bins, collection vehicles and illegal dumping. These issues are identified based on the survey performed in a city and analysis of related research studies and scientific reports. We determined that illegal dumping is one of these major concerns and needs a technological solution. In the fourth section, we propose a multipath convolutional neural network (mp-CNN) to detect and localize the waste dumps on streets and roadsides. We constructed our dataset to train and test the proposed model, as no benchmark dataset is publicly available to obtain this objective. We applied the weakly supervised learning approach to training the model. In this approach, mp-CNN was trained according to the image class; in our case, it is two (waste and non-waste). In the testing phase, the model showed the performance evaluation matrices 97.82% of precision, 98.86% of recall, 98.34% of F1 score, 98.33% of accuracy, and 98.63% of AUROC for this binary classification. Due to the scarcity of benchmark datasets, waste localization results cannot be presented quantitatively. So, we performed a survey to compare the overlapping of the mask generated by the model with the region waste in the actual image. The average score for the generated mask obtained a score of 3.884 on a scale of 5. Based on the analysis of model performance evaluation parameters, precision-recall curve, receiver characteristic operator curve, and comparison of mask generated by the model over waste with corresponding actual images show that mp-CNN performs remarkably good in detection, classification, and localization of waste regions. Finally, two conceptual architectures in the context of developing countries are suggested to demonstrate the future practical applications of the mp-CNN model.