Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare

2024-05-10

AI Summary

To address the lack of automated material identification and counting for solid medical waste (e.g., inhalers) in circular healthcare systems—causing unstable raw material supply and inefficient waste management—this paper proposes the first real-time, synchronous object detection framework tailored for circular healthcare. Our method introduces the novel concept of “synchromaterial,” enabling synchronized material perception via dual-visual-unit temporal fusion (12–22 FPS), multi-view visual alignment, a lightweight YOLO variant, and embedded edge deployment. The framework supports autonomous material sorting, spatial mapping, and dynamic counting. Evaluated on real-world scenarios, the prototype accurately identifies four critical materials across five inhaler types. We publicly release a benchmark dataset, source code, and demonstration video. Experimental results validate the approach’s feasibility, real-time performance, and scalability for large-scale, heterogeneous material monitoring in circular healthcare workflows.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Object Detection & Categorization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in general, something that is not measured cannot be effectively managed. In this paper, we propose a real-time synchronized object detection framework that enables, at the same time, autonomous sorting, mapping, and quantification of solid materials. We begin by introducing the general framework for real-time wide-area material monitoring, and then, we illustrate it using a numerical example. Finally, we develop a first prototype whose working principle is underpinned by the proposed framework. The prototype detects 4 materials from 5 different models of inhalers and, through a synchronization mechanism, it combines the detection outputs of 2 vision units running at 12-22 frames per second (Fig. 1). This led us to introduce the notion of synchromaterial and to conceive a robotic waste sorter as a node compartment of a material network. Dataset, code, and demo videos are publicly available.

Problem

• Automated Identification
• Circular Healthcare
• Solid Waste Management

Innovation

• Synchronized Multi-source Data Integration
• Automated Material Classification
• Circular Economy Material Tracking