Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry

• URL: http://arxiv.org/abs/2110.03384v1
• Date: Thu, 7 Oct 2021 12:23:00 GMT
• Title: Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry
• Authors: Anass El Houd, Charbel El Hachem, Loic Painvin
• Abstract summary: This work aims to apprehend and emphasize the contribution of deep learning model explainability to the improvement of welding seams classification accuracy and reliability. We implement a novel hybrid method that relies on combining the model prediction scores and visual explanation heatmap of the model. The results show that the hybrid model performance is relatively above our target performance and helps to increase the accuracy by at least 18%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The welding seams visual inspection is still manually operated by humans in different companies, so the result of the test is still highly subjective and expensive. At present, the integration of deep learning methods for welds classification is a research focus in engineering applications. This work intends to apprehend and emphasize the contribution of deep learning model explainability to the improvement of welding seams classification accuracy and reliability, two of the various metrics affecting the production lines and cost in the automotive industry. For this purpose, we implement a novel hybrid method that relies on combining the model prediction scores and visual explanation heatmap of the model in order to make a more accurate classification of welding seam defects and improve both its performance and its reliability. The results show that the hybrid model performance is relatively above our target performance and helps to increase the accuracy by at least 18%, which presents new perspectives to the developments of deep Learning explainability and interpretability.

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