Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry

• URL: http://arxiv.org/abs/2110.09023v1
• Date: Mon, 18 Oct 2021 05:43:06 GMT
• Title: Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry
• Authors: Patrick Hemmer, Niklas K\"uhl, Jakob Sch\"offer
• Abstract summary: We propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings. By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 to using such systems in practice. In this paper, we propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings without compromising performance. By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized.

Related papers

• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• A Car Model Identification System for Streamlining the Automobile Sales Process [0.0]
  This project presents an automated solution for the efficient identification of car models and makes from images. We achieved a notable accuracy of 81.97% employing the EfficientNet (V2 b2) architecture. The trained model offers the potential for automating information extraction, promising enhanced user experiences across car-selling websites.
  arXiv  Detail & Related papers  (2023-10-19T23:36:17Z)
• Hardware Accelerators in Autonomous Driving [5.317893030884531]
  Hardware accelerators are special-purpose coprocessors that help autonomous vehicles meet performance requirements for higher levels of autonomy. This paper provides an overview of ML accelerators with examples of their use for machine vision in autonomous vehicles.
  arXiv  Detail & Related papers  (2023-08-11T10:07:33Z)
• Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML [52.86328317233883]
  We present a comprehensive overview of different ways in which fairness-related harm can arise. We highlight several open technical challenges for future work in this direction.
  arXiv  Detail & Related papers  (2023-03-15T09:40:08Z)
• A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications [0.0]
  This paper presents a feature-based algorithm to detect certain effects that can degrade image quality in automotive applications. Experiments with different data sets show that the algorithm can detect soiling adhering to camera lenses and classify different types of image degradation.
  arXiv  Detail & Related papers  (2023-03-13T13:40:09Z)
• Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers [105.89564687747134]
  We propose a self-regularized AutoAugment method to learn views for self-supervised vision transformers. First, we reduce the search cost of AutoView to nearly zero by learning views and network parameters simultaneously. We also present a curated augmentation policy search space for self-supervised learning.
  arXiv  Detail & Related papers  (2022-10-16T06:20:44Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images [58.287120077778205]
  We introduce a framework for analyzing robustness of the learning algorithm w.r.t varying quality in the image input for autonomous driving. Using the results of sensitivity analysis, we propose an algorithm to improve the overall performance of the task of "learning to steer"
  arXiv  Detail & Related papers  (2021-02-26T02:08:07Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Deep Learning Models for Visual Inspection on Automotive Assembling Line [2.594420805049218]
  This paper proposes the use of deep learning-based methodologies to assist in visual inspection tasks. The proposed approach is illustrated by four proofs of concept in a real automotive assembly line.
  arXiv  Detail & Related papers  (2020-07-02T20:00:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.