Scene understanding pipeline for maintenance oriented tasks automation

Younes Zegaoui; Sébastien Dufour; Christophe Bortolaso

doi:10.1117/12.2692213

28 July 2023 Scene understanding pipeline for maintenance oriented tasks automation

Younes Zegaoui, Sébastien Dufour, Christophe Bortolaso

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490R (2023) https://doi.org/10.1117/12.2692213
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

Computerized Maintenance Management System (CMMS) are to assist in organising maintenance, both proactive and reactive, as well as technical operations. It usually works alongside constant surveillance and monitoring of said equipment through repetitive and time-consuming tasks. AI can ease maintenance activities by reducing the time spent on these repetitive task and allocate more time on decision. In this article we present our works on automating part of the intervention request handling in Berger-Levrault’s CMMs. We designed a pipeline of computer vision operations to predict the type of intervention needed from a picture of the situation at hand. The pipeline is basically a decision tree which combines different computer vision models and funnel images between them according to their respective outputs. Each of these models are trained separately on a specific task. To validate our approach, we performed a topic modeling analysis on the maintenance request forms to identify the ten most common topics of intervention. We show that our pipeline performs better than direct prediction by scene recognition model with a five points increases in global F1 score (40% / 45%) which is even more true for the classes with fewer training examples (23% / 37%).

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Younes Zegaoui, Sébastien Dufour, and Christophe Bortolaso "Scene understanding pipeline for maintenance oriented tasks automation", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490R (28 July 2023); https://doi.org/10.1117/12.2692213

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KEYWORDS

Object detection

Education and training

Data modeling

Image classification

Computer vision technology

Scene classification

Image processing

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