An Integrated System for WEEE Sorting Employing X-ray Imaging, AI-based Object Detection and Segmentation, and Delta Robot Manipulation
- URL: http://arxiv.org/abs/2512.05599v1
- Date: Fri, 05 Dec 2025 10:36:33 GMT
- Title: An Integrated System for WEEE Sorting Employing X-ray Imaging, AI-based Object Detection and Segmentation, and Delta Robot Manipulation
- Abstract summary: Battery recycling is becoming increasingly critical due to the rapid growth in battery usage and the limited availability of natural resources.<n>As battery energy densities continue to rise, improper handling during recycling poses significant safety hazards, including potential fires at recycling facilities.<n>We present our novel approach which integrates a specialized X-ray transmission dual energy imaging subsystem with advanced pre-processing algorithms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Battery recycling is becoming increasingly critical due to the rapid growth in battery usage and the limited availability of natural resources. Moreover, as battery energy densities continue to rise, improper handling during recycling poses significant safety hazards, including potential fires at recycling facilities. Numerous systems have been proposed for battery detection and removal from WEEE recycling lines, including X-ray and RGB-based visual inspection methods, typically driven by AI-powered object detection models (e.g., Mask R-CNN, YOLO, ResNets). Despite advances in optimizing detection techniques and model modifications, a fully autonomous solution capable of accurately identifying and sorting batteries across diverse WEEEs types has yet to be realized. In response to these challenges, we present our novel approach which integrates a specialized X-ray transmission dual energy imaging subsystem with advanced pre-processing algorithms, enabling high-contrast image reconstruction for effective differentiation of dense and thin materials in WEEE. Devices move along a conveyor belt through a high-resolution X-ray imaging system, where YOLO and U-Net models precisely detect and segment battery-containing items. An intelligent tracking and position estimation algorithm then guides a Delta robot equipped with a suction gripper to selectively extract and properly discard the targeted devices. The approach is validated in a photorealistic simulation environment developed in NVIDIA Isaac Sim and on the real setup.
Related papers
- Integrated Multimodal AI System for Retrieval-Augmented Reasoning, Object Sensing, and Damage Analysis [4.6225800903606515]
This work presents a unified multimodal AI system for damage assessment.<n>It integrates retrieval-augmented generation (RAG) models, thermal spectrum perception, vision foundation model pipelines, and exploratory wireless signal sensing.
arXiv Detail & Related papers (2026-08-09T21:53:37Z) - TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors [76.4118981404995]
Trajectory-Injected Generative Attack (TIGA) is a source-image-free and training free framework that generates detector-evasive images.<n>TIGA steers the latent Denoising Diffusion Implicit Model (DDIM) trajectory so that adversarial properties emerge during generation.<n>TIGA achieves strong blackbox attack performance, transferability, and high robustness under common post-processing operations.
arXiv Detail & Related papers (2026-07-28T15:51:39Z) - GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments [63.222920701596564]
Training embodied agents in the real world requires skilled operators and expensive hardware.<n> Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation.<n>Fastly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning.
arXiv Detail & Related papers (2026-06-16T05:00:42Z) - A Hyperspectral Imaging Guided Robotic Grasping System [7.094813708058253]
We introduce a novel hyperspectral imaging-guided robotic grasping system.<n> PRISM is designed to enable high-precision, distortion-free hyperspectral imaging.<n>The proposed system demonstrates substantial improvements in both textile recognition compared to human performance.
arXiv Detail & Related papers (2025-12-05T09:58:59Z) - Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset [8.578006502996516]
We introduce textbfElectrolyzers-HSI, a novel multimodal benchmark dataset designed to accelerate the recovery of critical raw materials.<n>The dataset comprises 55 co-registered high-resolution RGB images and hyperspectral imaging (HSI) data cubes spanning the 400--2500 nm spectral range.<n>This enables non-invasive spectral analysis of shredded electrolyzer samples, supporting quantitative and qualitative material classification and spectral properties investigation.
arXiv Detail & Related papers (2025-05-26T20:16:38Z) - Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
arXiv Detail & Related papers (2025-05-06T06:46:19Z) - Multi-modal Multi-platform Person Re-Identification: Benchmark and Method [58.59888754340054]
MP-ReID is a novel dataset designed specifically for multi-modality and multi-platform ReID.<n>This benchmark compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging.<n>We introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios.
arXiv Detail & Related papers (2025-03-21T12:27:49Z) - Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object Detection [15.374716710970107]
This paper builds a brain-eye-computer based object detection system for aerial images under few-shot conditions.<n>An adaptive modality balanced online knowledge distillation (AMBOKD) method is proposed to recognize dim objects with the EEG-image data.<n>Experiments conducted on public datasets and system validations in real-world scenarios demonstrate the effectiveness and superiority of our method.
arXiv Detail & Related papers (2024-07-02T02:30:23Z) - Random resistive memory-based deep extreme point learning machine for
unified visual processing [67.51600474104171]
We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM)
Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
arXiv Detail & Related papers (2023-12-14T09:46:16Z) - Robotic Navigation Autonomy for Subretinal Injection via Intelligent
Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
We propose a framework for autonomous robotic navigation for subretinal injection.
Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target.
Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
arXiv Detail & Related papers (2023-01-17T21:41:21Z) - Real-time Non-line-of-sight Imaging with Two-step Deep Remapping [0.0]
Non-line-of-sight (NLOS) imaging takes the indirect light into account.
Most solutions employ a transient scanning process, followed by a back-projection based algorithm to reconstruct the NLOS scenes.
Here we propose a new NLOS solution to address the above defects, with innovations on both detection equipment and reconstruction algorithm.
arXiv Detail & Related papers (2021-01-26T00:08:54Z)
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.