CleanSentry - Deep Learning-Based Beach Waste Material Classification and Report Generation System on Colombo District Seaside in Sri Lanka

Abstract

"Waste pollution in Sri Lanka's coastal regions poses a significant challenge, affecting marine ecosystems, residents, tourism, and the environment. Addressing this issue necessitates innovative solutions to enhance sustainability efforts and optimize waste management processes. This research utilizes machine learning techniques to address the pressing need for effective waste classification. By leveraging convolutional neural networks (CNNs), the proposed solution aims to automate waste identification and categorization, thereby improving waste sorting practices. The methodology involves developing and training a CNN model using preprocessed drone-captured waste images. The MobileNetV2 model architecture, stripped of its top layer, is augmented with custom layers for global average pooling and a dense (output) layer, tailored to the specific classification task. The model achieves an accuracy of 80.74%, indicating its effectiveness in correctly classifying waste instances. However, the research faced limitations due to limited data availability and varying image data quality, impacting model robustness. The scope was further limited to generating classification reports rather than implementing real-time alerts, affecting immediate responsiveness. Future enhancements include implementing multiple object detection, integrating real-time reporting functionality, and incorporating geolocation mapping for enhanced waste management."