Development of Computer Vision and Deep Learning Based Algorithm to Improve Waste Management System

Noteworthy steps are being taken towards improving the level of hygiene and cleanliness in cities. Even with these efforts littering and non-recyclable waste is still a serious maj or issue socially and ecologically. Locating, identifying, and picking up the waste by staff can be a tiresome and inefficient task requiring long hours of manpower. Computer Vision and Deep Learning techniques can help solve this major problem by applying detection algorithms which can ease the task of analyzing such images for the benefit of humans. To address the problem of locating and identification of the waste objects we propose a cost-effective method which is based on application detection algorithms on low altitude imagery from streets, sidewalks, houses, and landfills taken by surveillance camera or Unmanned aerial vehicles or UAVs. The deep learning-based framework consists of deep convolutional neural networks which perform the localization and the classification task which can then be processed and put through into automated pickup planning system. A dataset was also compiled and annotated to train and evaluate the model. The model achieved a mAP score of 85% when tested on 5 classes while testing on all classes a mAP score of 78% was obtained.