StreetView-Waste: A Multi-Task Dataset for Urban Waste Management
Diogo J. Paulo, Jo\~ao Martins, Hugo Proen\c{c}a, Jo\~ao C. Neves

TL;DR
StreetView-Waste introduces a comprehensive multi-task dataset for urban waste management, enabling detection, tracking, and segmentation of waste containers and litter from real-world images, with baseline models and improved strategies.
Contribution
The paper presents a new dataset, StreetView-Waste, with annotations for multiple tasks and proposes strategies to enhance detection, tracking, and segmentation performance in urban waste scenarios.
Findings
Heuristic tracking reduces counting error by 79.6%.
Geometry-aware segmentation improves [email protected] by 27%.
Baseline models perform reasonably but still face challenges.
Abstract
Urban waste management remains a critical challenge for the development of smart cities. Despite the growing number of litter detection datasets, the problem of monitoring overflowing waste containers, particularly from images captured by garbage trucks, has received little attention. While existing datasets are valuable, they often lack annotations for specific container tracking or are captured in static, decontextualized environments, limiting their utility for real-world logistics. To address this gap, we present StreetView-Waste, a comprehensive dataset of urban scenes featuring litter and waste containers. The dataset supports three key evaluation tasks: (1) waste container detection, (2) waste container tracking, and (3) waste overflow segmentation. Alongside the dataset, we provide baselines for each task by benchmarking state-of-the-art models in object detection, tracking, and…
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Taxonomy
TopicsAdvanced Neural Network Applications · Municipal Solid Waste Management · Mobile Crowdsensing and Crowdsourcing
