OF3D: optical flow to 3D CNN for hoisting status classification

Liukailai Ding; Lizuo Jin

doi:10.1117/12.3035350

19 July 2024 OF3D: optical flow to 3D CNN for hoisting status classification

Liukailai Ding, Lizuo Jin

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133S (2024) https://doi.org/10.1117/12.3035350
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

With the advancement of industrial automation, crewless operation has become a primary requirement for achieving "dark factory" requirements. This paper introduces a video-based understanding algorithm for crane hoisting status detection within industrial settings. An I3D-ResNet network enhanced with optical flow features is employed to classify the hoisting status. This algorithmic process can precisely capture and understand the dynamic changes during the hoisting process, significantly improving event recognition accuracy. Compared to using the I3D-ResNet network alone, the optical flow-enhanced network has demonstrated through experimental results its effectiveness in accurately recognizing hoisting status, providing robust support for achieving crewless production in industrial environments in the near future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liukailai Ding and Lizuo Jin "OF3D: optical flow to 3D CNN for hoisting status classification", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133S (19 July 2024); https://doi.org/10.1117/12.3035350

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