SortWaste: A Densely Annotated Dataset for Object Detection in Industrial Waste Sorting

• URL: http://arxiv.org/abs/2601.02299v2
• Date: Wed, 07 Jan 2026 12:06:35 GMT
• Title: SortWaste: A Densely Annotated Dataset for Object Detection in Industrial Waste Sorting
• Authors: Sara Inácio, Hugo Proença, João C. Neves,
• Abstract summary: Manual waste sorting is inefficient for handling large-scale waste streams.<n>Existing automated sorting approaches struggle with the high variability, clutter, and visual complexity of real-world waste streams.
• Score: 5.931399156681511
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing production of waste, driven by population growth, has created challenges in managing and recycling materials effectively. Manual waste sorting is a common practice; however, it remains inefficient for handling large-scale waste streams and presents health risks for workers. On the other hand, existing automated sorting approaches still struggle with the high variability, clutter, and visual complexity of real-world waste streams. The lack of real-world datasets for waste sorting is a major reason automated systems for this problem are underdeveloped. Accordingly, we introduce SortWaste, a densely annotated object detection dataset collected from a Material Recovery Facility. Additionally, we contribute to standardizing waste detection in sorting lines by proposing ClutterScore, an objective metric that gauges the scene's hardness level using a set of proxies that affect visual complexity (e.g., object count, class and size entropy, and spatial overlap). In addition to these contributions, we provide an extensive benchmark of state-of-the-art object detection models, detailing their results with respect to the hardness level assessed by the proposed metric. Despite achieving promising results (mAP of 59.7% in the plastic-only detection task), performance significantly decreases in highly cluttered scenes. This highlights the need for novel and more challenging datasets on the topic.

Related papers

• Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks [55.27114962330541]
  We propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task.<n>Deep neural networks achieve remarkable performance but have time and storage-consuming training processes.
  arXiv  Detail & Related papers  (2026-01-15T05:29:50Z)
• StreetView-Waste: A Multi-Task Dataset for Urban Waste Management [5.429555343961488]
  StreetView-Waste is a comprehensive dataset of urban scenes featuring litter and waste containers.<n>The dataset supports three key evaluation tasks: (1) waste container detection, (2) waste container tracking, and (3) waste segmentation.
  arXiv  Detail & Related papers  (2025-11-20T15:10:33Z)
• WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks [7.775894876221921]
  We introduce a data augmentation method based on a novel GAN architecture called wasteGAN. The proposed method allows to increase the performance of semantic segmentation models, starting from a very limited bunch of labeled examples. We then leverage the higher-quality segmentation masks predicted from models trained on the wasteGAN synthetic data to compute semantic-aware grasp poses.
  arXiv  Detail & Related papers  (2024-09-25T15:04:21Z)
• SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation [46.178512739789426]
  We present SpectralWaste, the first dataset collected from an operational plastic waste sorting facility. This dataset contains labels for several categories of objects that commonly appear in sorting plants. We propose a pipeline employing different object segmentation architectures and evaluate the alternatives on our dataset.
  arXiv  Detail & Related papers  (2024-03-26T18:39:38Z)
• VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting [61.52419223232737]
  In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream. We present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
  arXiv  Detail & Related papers  (2023-03-26T21:38:38Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis [72.85526892440251]
  We introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The proposed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties.
  arXiv  Detail & Related papers  (2022-08-08T08:15:34Z)
• A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth [61.90504318229845]
  This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene. A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
  arXiv  Detail & Related papers  (2022-06-13T19:14:06Z)
• Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark [53.9819155669618]
  This paper presents a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection. With an intensive amount of effort, our dataset contains $12$ categories of prohibited items in $47,677$ X-ray images with high-quality annotated segmentation masks and bounding boxes. The proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items.
  arXiv  Detail & Related papers  (2021-08-16T11:14:16Z)
• ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
  We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over1800fully segmented video frames collected from a real waste sorting plant. We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
  arXiv  Detail & Related papers  (2021-06-04T22:17:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.