Design of Waste Management System Using Ensemble Neural Networks

Abstract

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.

Keywords

• Structure from Motion
• aggregated residual transformations network
• trash classification
• scale invariant feature transform
• structural similarity index