Using Artificial Intelligence to Facilitate Assembly Automation in High-Mix Low-Volume Production Scenario

Simeth, Alexej; Kumar, Atal Anil; Plapper, Peter

doi:10.1016/j.procir.2022.05.103

Cited by 2 publications

(1 citation statement)

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“…YOLOv5 has made further improvements on the previous version, with increased detection accuracy and speed. Among its variants, YOLOv5s maintains excellent detection capability within a compact size (7.3 MB), laying the foundation for prospective explorations into hardware platforms for cost-effective and capacity-efficient logistics warehouse object tracking [11].…”

Section: Comparison Of the Three Detection Algorithmsmentioning

confidence: 99%

Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT

Xie,

Yao

2023

Applied Sciences

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The future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios has become a challenge. A pivotal technological solution for dealing with such a challenge is to distinguish and track moving objects such as humans and goods. Therefore, an algorithm model combining YOLOv5 and DeepSORT for logistics warehouse object tracking is designed, where YOLOv5 is selected as the object-detection algorithm and DeepSORT distinguishes humans from goods and environments. The evaluation metrics from the MOT Challenge affirm the algorithm’s robustness and efficacy. Through rigorous experimental tests, the combined algorithm demonstrates rapid convergence (within 30 ms), which holds promising potential for applications in real-world logistics warehouses.

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Section: Comparison Of the Three Detection Algorithmsmentioning

confidence: 99%