StreetView-Waste: A Multi-Task Dataset for Urban Waste Management

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 mAP@0.5 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 segmentation. Additionally, we enhance baseline performance by proposing two complementary strategies: a heuristic-based method for improved waste container tracking and a model-agnostic framework that leverages geometric priors to refine litter segmentation. Our experimental results show that while fine-tuned object detectors achieve reasonable performance in detecting waste containers, baseline tracking methods struggle to accurately estimate their number; however, our proposed heuristics reduce the mean absolute counting error by 79.6%. Similarly, while segmenting amorphous litter is challenging, our geometry-aware strategy improves segmentation mAP@0.5 by 27% on lightweight models, demonstrating the value of multimodal inputs for this task. Ultimately, StreetView-Waste provides a challenging benchmark to encourage research into real-world perception systems for urban waste management.