Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry
Faurecia Clean Mobility（佛吉亚清洁出行）；Ecole Centrale de Lyon（里昂中央理工学院）；FEMTO-ST Institute, CNRS（法国国家科学研究中心FEMTO-ST研究所）；Univ. Bourgogne Franche-Comte (UBFC)（勃艮第弗朗什-孔泰大学）
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.
Federal University of Tecnology - Paraná (UTFPR)（巴拉那联邦理工大学）
Automotive manufacturing assembly tasks are built upon visual inspections such as scratch identification on machined surfaces, part identification and selection, etc, which guarantee product and process quality. These tasks can be related to more than one type of vehicle that is produced within the same manufacturing line. Visual inspection was essentially human-led but has recently been supplemented by the artificial perception provided by computer vision systems (CVSs). Despite their relevance, the accuracy of CVSs varies accordingly to environmental settings such as lighting, enclosure and …
Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer customers the possibility to configure their car online according to their personal preferences. However, human-led quality assurance faces the challenge to keep up with high-volume visual inspections due to the car models' increasing complexity. Even though the application of machine learning to many visual inspection tasks has demonstrated great success, its need for large labeled data sets remains a central barrier t…
Czech Technical University in Prague（捷克技术大学）；IBM Research Zürich（IBM苏黎世研究院）
Labeling images for visual segmentation is a time-consuming task which can be costly, particularly in application domains where labels have to be provided by specialized expert annotators, such as civil engineering. In this paper, we propose to use attribution methods to harness the valuable interactions between expert annotators and the data to be annotated in the case of defect segmentation for visual inspection of civil infrastructures. Concretely, a classifier is trained to detect defects and coupled with an attribution-based method and adversarial climbing to generate and refine segmentat…
Handong Global University（韩东国际大学）；Virginia Tech（弗吉尼亚理工大学）
A misalignment of LiDAR as low as a few degrees could cause a significant error in obstacle detection and mapping that could cause safety and quality issues. In this paper, an accurate inspection system is proposed for estimating a LiDAR alignment error after sensor attachment on a mobility system such as a vehicle or robot. The proposed method uses only a single target board at the fixed position to estimate the three orientations (roll, tilt, and yaw) and the horizontal position of the LiDAR attachment with sub-degree and millimeter level accuracy. After the proposed preprocessing steps, the…
Real-Time Wheel Detection and Rim Classification in Automotive Production
Charles University（查理大学）；Czech Technical University in Prague（布拉格捷克理工大学）；Institute of Information Theory and Automation, The Czech Academy of Sciences（捷克科学院信息理论与自动化研究所）
This paper proposes a self-explainable Deep Learning (SE-DL) system for an image classification problem that performs self-error detection. The self-error detection is key to improving the DL system's safe operation, especially in safety-critical applications such as automotive systems. A SE-DL system outputs both the class prediction and an explanation for that prediction, which provides insight into how the system makes its predictions for humans. Additionally, we leverage the explanation of the proposed SE-DL system to detect potential class prediction errors of the system. The proposed SE-…
University of Cincinnati（辛辛那提大学）；Clemson University（克莱姆森大学）；BMW Group（宝马集团）
Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neura…
A Dataset and Baseline for Deep Learning-Based Visual Quality Inspection in Remanufacturing
Institute for Machine Tools and Industrial Management (iwb), Technical University of Munich（慕尼黑工业大学机床与工业管理研究所）；MINES Paris, PSL University, Centre for Mathematical Morphology (CMM)（巴黎高科矿业学院，巴黎文理研究大学，数学形态学中心）；Fraunhofer Institute for Casting, Composite and Processing Technology IGCV（弗劳恩霍夫铸造、复合材料与加工技术研究所）
A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws. Human-only visual inspection can be very time-consuming and laborious, and is a significant bottleneck especially for high-throughput manufacturing scenarios. Given significant advances in the field of deep learning, automated visual quality inspection can lead to highly efficient and reliable detection of defects and flaws during the manufacturing process. However, deep learning-driven visual inspection methods often necessitate significant computational resources,…
Zero-Shot Multi-Criteria Visual Quality Inspection for Semi-Controlled Industrial Environments via Real-Time 3D Digital Twin Simulation
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Costa Rica Institute of Technology（哥斯达黎加理工学院）；Gestalt Automation GmbH（Gestalt Automation有限公司）；Technical University Berlin（柏林工业大学）
Image class translation: visual inspection of class-specific hypotheticals and classification based on translation distance
College of Natural Sciences, Colorado State University, Colorado, United States of America（科罗拉多州立大学自然科学院）；School of Biomedical and Chemical Engineering, Colorado State University, Colorado, United States of America（科罗拉多州立大学生物医学与化学工程学院）；Department of Electrical and Computer Engineering, Colorado State University, Colorado, United States of America（科罗拉多州立大学电气与计算机工程学院）
Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehicle Dent Detection
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST)（电气工程与计算机科学学院 国家科学与技术大学）；Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano（电子信息与生物工程系 米兰理工学院）
