Transforming urban waste collection inventory: AI-Based container classification and Re-Identification

This work lays the groundwork for creating an automated system for the inventory of urban waste elements. Our primary contribution is the development of, to the best of our knowledge, the first re-identification system for urban waste elements that uses Artificial Intelligence and Computer Vision, incorporating information from a classification module and geolocation context to enhance post-processing performance. This re-identification system helps to create and update inventories by determining if a new image matches an existing element in the inventory based on visual similarity or, if not, by adding it as a new identity (new class or new identity of an existing class). Such a system could be highly valuable to local authorities and waste management companies, offering improved facility maintenance, geolocation, and additional applications. This work also addresses the dynamic nature of urban environments and waste management elements by exploring Continual Learning strategies to adapt pretrained systems to new settings with different urban elements. Experimental results show that the proposed system operates effectively across various container types and city layouts. These findings were validated through testing in two different Spanish locations, a "City" and a "Campus", differing in size, illumination conditions, seasons, urban design and container appearance. For the final re-identification system, the baseline system achieves 53.18 mAP (mean Average Precision) in the simple scenario, compared to 21.54 mAP in the complex scenario, with additional challenging unseen variability. Incorporating the proposed post-processing techniques significantly improved results, reaching 74.14 mAP and 71.75 mAP in the simple and complex scenario respectively.