Organic waste management is a crucial component of a circular economy, which prioritizes reducing waste through the reuse and recycling of products and materials. It is a tedious and complicated task, largely accomplished through manual labor. We introduce a novel ‘in-the-wild’ multimodal image dataset of 15-band NIR multi-spectral and single band thermal images of bulk food waste in an industrial setting. The dataset showcases a number of complex computer vision problems that are unavoidable constraints in this setting. Benchmarking against different computer vision algorithms is performed to highlight these challenges. The key issues and their place in robotic waste processing for industrial applications, and grand challenge objectives are discussed.
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Matthew Vestal
Research Student
Matthew Vestal is a PhD candidate at the University of Canberra robotics lab. His research interests include using computer vision and AI to address climate and environmental issues, with research experience in computer vision and also oceanography. His current research incorporates multimodal imaging systems to detect contaminants like plastic in industrial scale food waste.
Matthew's Publications
ICMI’25 Grand Challenge: A Thermal and Spectral Multimodal Image Dataset for Contaminant Detection in Industrial Organic Food Waste