Waste Detection

Waste detection research focuses on automatically identifying and mapping various types of waste using computer vision and machine learning techniques, primarily leveraging remote sensing imagery (aerial and satellite) and, increasingly, underwater imaging. Current efforts concentrate on improving model accuracy and robustness across diverse image resolutions and conditions (e.g., underwater distortions, low-resolution aerial photos), often employing deep learning architectures like Convolutional Neural Networks (CNNs) and exploring techniques like super-resolution enhancement. This work is crucial for environmental monitoring and management, enabling efficient identification of illegal dumping sites, tracking plastic pollution, and informing waste management strategies.