@inproceedings{dc94acbc2a23400fb353462c743a15eb,
title = "Impact of Illuminance on Object Detection in Industrial Vision Systems Using Neural Networks",
abstract = "Accuracy and classification of vision systems employed in industry are impacted by illumination. Varying levels of illumination effect a scene, thus changing how the vision system 'sees' the object. Depending how a model was trained and under what condition, this may cause a vision system to lose confidence and incorrectly classify objects. This research considers the effect of illuminance on vision systems, taking into account the recommended lumen levels of industrial workspaces and considering how best to train a neural network for a vision system based on the findings. A vision system using convolutional neural networks was trained under standard office lighting conditions to recognise an industrial component. The vision system was tested at incrementing lux intervals for presence and absence of the component. The vision system failed to classify correctly when tested under industrial workshop lighting settings. A statistically significant relationship was found indicating a negative linear relationship between illuminance and model confidence.",
keywords = "illumination, industry 4, neural nets, object detection, vision systems, workplace lighting",
author = "Jacqueline Humphries and Kelly O'Brien and Luke O'Brien and Nathan Dunne",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2nd International Conference on Artificial Intelligence, Robotics and Control, AIRC 2020 ; Conference date: 12-12-2020 Through 14-12-2020",
year = "2020",
month = dec,
day = "12",
doi = "10.1145/3448326.3448330",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "24--28",
booktitle = "2020 2nd International Conference on Artificial Intelligence, Robotics and Control, AIRC 2020",
address = "United States",
}