Neural networks for back wall geometry reconstruction using the active thermography

Halloua, Hicham; Obbadi, Abdellatif; Errami, Youssef; Sahnoun, Smail; Elhassnaoui, Ahmed

doi:10.1109/cistem.2016.8066775

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Maldague [13] provides an example of using trained neural networks to detect corrosion in aluminum specimens. Halloua [14] proposes a method to create 3D reconstruction of the subsurface depth geometry by training a neural network to estimate the depths of subsurface defects. In [15], the authors train supervised learning algorithms to identify bruised apples based on the thermal responses.…”

Section: Related Work a Active Thermographymentioning

confidence: 99%

Towards Material Classification of Scenes Using Active Thermography

Bai

Bhattacharjee

Chen

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

By briefly heating the local environment with a heat lamp and observing what happens with a thermal camera, robots could potentially infer properties of their surroundings. However, this form of active thermography introduces large signal variations compared to traditional active thermography, which has typically been used to characterize small regions of materials in carefully controlled settings. We demonstrate that a data-driven approach with modern machine learning methods can be used to classify material samples over relatively large surface areas and variable distances. We also introduce the use of z-normalization to improve material classification and reduce variation due to distance and heating intensity. Our best performing algorithm achieved an overall accuracy of 77.7% for multi-class classification among 12 materials placed at varying distances (20 cm, 30 cm, and 40 cm). The observations were made for 5 seconds with 1s of heating and 4s of cooling. We also provide a demonstration of performance with a multi-material scene.

show abstract

Section: Related Work a Active Thermographymentioning

confidence: 99%