Garbage Monitoring And Management Using Deep Learning

Abstract

Rapid urbanisation and population growth have led to an unprecedented increase in waste generation. In addition to this, increasing tourism has also increased the challenge of maintaining coastal areas. Inefficient and inadequate waste management practices pose significant environmental and health hazards to both humans and wildlife. Through deep learning and computer vision techniques, the garbage can be identified and its location can be extracted directly from the images. Videos are collected using UAVs. Auto generation of waste reports and additional services like chat-bots are also implemented. Furthermore, the system implements OR tools using which the routes of garbage collector vehicles is optimised. By minimising travel distances and maximising cleanup efficiency, the system reduces operational costs and enhances the overall effectiveness of beach cleanup initiatives. Predominant spots of garbage are analysed and the nearest dustbins are mapped along with the route to reach the dustbin. The garbage detection model gave a mAP of 0.845. The silhouette score of clustering was 70.1% for chameleon and 99.02% for k means. All of the above mentioned modules were integrated and presented on the user interface of the application developed.