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Exposure to hazardous waste substances has been linked to increased rates of multiple forms of cancer. Traditional inspection methods require time and manpower. Advances in UAV technology, combined with AI-enabled Computer Vision, offer a promising solution to significantly reduce survey time and the required workforce. The main contributions of this work are the evaluation of two Object Detection models, YOLOv8 and Faster R-CNN, trained to identify 7 waste materials from UAV imagery and the presentation of a practical pipeline to implement the proposed model in environmental agency workflows. To the best of our knowledge, no existing dataset or model has been designed to detect such a diverse range of waste types from UAV images. Results suggest that Object Detection is highly effective for regularly shaped waste categories such as <jats:italic>Textile, Pallets</jats:italic> and <jats:italic>Tires</jats:italic>, with the best YOLOv8 model achieving AP scores of 80.22%, 69.24% and 62.47% respectively. However, for waste materials with irregular boundaries, such as <jats:italic>Rubble</jats:italic> or <jats:italic>Mixed Items</jats:italic>, detection remains challenging. 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