Deep learning approach for a machine-human interface based on optical real-time gesture recognition for automated guided vehicles

Kiran Raj Krishnakumar; Laura Gersmeier; Leif Ole Harders; Stephan Hussmann

doi:10.1117/12.3016404

20 June 2024 Deep learning approach for a machine-human interface based on optical real-time gesture recognition for automated guided vehicles

Kiran Raj Krishnakumar, Laura Gersmeier, Leif Ole Harders, Stephan Hussmann

Author Affiliations +

Proceedings Volume 13000, Real-time Processing of Image, Depth, and Video Information 2024; 1300009 (2024) https://doi.org/10.1117/12.3016404
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

The intersection of deep learning and programmable logic controllers (PLCs) can lead to innovative applications in automation. One of the exciting application areas are gesture-based control systems for Automated Guided Vehicles (AGVs). AGVs are used in various industries for material handling, logistics, warehouse automation, etc. Traditionally, these vehicles are controlled using predefined routes or remote controls, but with gesture-based control, operators can communicate more naturally and efficiently. The incorporation of YOLO-Pose in YOLO versions 7 and 8 has elevated the YOLO algorithm to a leading tool for creating gesture recognition models. The YOLO algorithm employs convolutional neural networks (CNN) to detect objects in real-time. These latest YOLO models offer significantly improved accuracy, speed, and reduced training times. This paper presents the comparative results of 2D gesture recognition transfer learning models created using the YOLO v5, v7, and v8 models, along with the steps taken to implement the model in a PLC-controlled AGV.

Conference Presentation

Citation Download Citation

Kiran Raj Krishnakumar, Laura Gersmeier, Leif Ole Harders, and Stephan Hussmann "Deep learning approach for a machine-human interface based on optical real-time gesture recognition for automated guided vehicles", Proc. SPIE 13000, Real-time Processing of Image, Depth, and Video Information 2024, 1300009 (20 June 2024); https://doi.org/10.1117/12.3016404

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