Sensors, Vol. 22, Pages 160: Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models

Sensors, Vol. 22, Pages 160: Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models Sensors doi: 10.3390/s22010160 Authors: Xuelin Zhang Donghao Zhang Alexander Leye Adrian Scott Luke Visser Zongyuan Ge Paul Bonnington This paper focuses on improving the performance of scientific instrumentation that uses glass spray chambers for sample introduction, such as spectrometers, which are widely used in analytical chemistry, by detecting incidents using deep convolutional models. The performance of these instruments can be affected by the quality of the introduction of the sample into the spray chamber. Among the indicators of poor quality sample introduction are two primary incidents: The formation of liquid beads on the surface of the spray chamber, and flooding at the bottom of the spray chamber. Detecting such events autonomously as they occur can assist with improving the overall operational accuracy and efficacy of the chemical analysis, and avoid severe incidents such as malfunction and instrument damage. In contrast to objects commonly seen in the real world, beading and flooding detection are more challenging since they are of significantly small size and transparent. Furthermore, the non-rigid property increases the difficulty of the detection of these incidents, as such that existing deep-learning-based object detection frameworks are prone to fail for this task. There is no former work that uses computer vision to detect these...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research