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Impact of Blemish Artefacts on Object Detection Models in Autonomous Driving: A Study on Camera Module Manufacturing Defects

  • With the rise of autonomous driving technology, concerns over the safety of AI-based systems have increased, particularly regarding their effect on human life and infrastructure. The complexity and unpredictable nature of AI-enabled software functions have disrupted traditional safety evaluation methods. In such a scenario, the quality control methods used at the manufacturing environments of sensors used for deploying AI-based perception functions need to be coupled with the downstream functions. This study focuses on investigating the impact of a specific production-related defect called blemish, which is caused by debris on image sensors of camera modules. The research aims to answer two fundamental questions: 1) How can blemish artifacts be mathematically modeled, and what is the process for developing a suitable dataset that incorporates this defect? 2) How can the influence of blemish artifacts on performance of object detection models be traced back to define quality requirements of the camera modules? This work takes a novelWith the rise of autonomous driving technology, concerns over the safety of AI-based systems have increased, particularly regarding their effect on human life and infrastructure. The complexity and unpredictable nature of AI-enabled software functions have disrupted traditional safety evaluation methods. In such a scenario, the quality control methods used at the manufacturing environments of sensors used for deploying AI-based perception functions need to be coupled with the downstream functions. This study focuses on investigating the impact of a specific production-related defect called blemish, which is caused by debris on image sensors of camera modules. The research aims to answer two fundamental questions: 1) How can blemish artifacts be mathematically modeled, and what is the process for developing a suitable dataset that incorporates this defect? 2) How can the influence of blemish artifacts on performance of object detection models be traced back to define quality requirements of the camera modules? This work takes a novel perspective by shifting the focus from improving model resilience to improving hardware quality camera modules by studying the effects of manufacturing defects on downstream functions. This research seeks to establish a framework to derive quality targets for front camera module manufacturing which can be extended to other sensors for similar applications.show moreshow less

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Metadaten
Author:Nitin Augustine, Maximilian Schwab, Steffen Klarmann, Christian Pfefferer, Alexander SchiendorferORCiD
Language:English
Document Type:Article
Conference:5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), Lisbon (Portugal), 22.-24.11.2023
Year of first Publication:2024
published in (English):Procedia Computer Science
Publisher:Elsevier
Place of publication:Amsterdam
ISSN:1877-0509
Volume:2024
Issue:232
First Page:616
Last Page:625
Review:peer-review
Open Access:ja
Version:published
URN:urn:nbn:de:bvb:573-46388
Related Identifier:https://doi.org/10.1016/j.procs.2024.01.061
Faculties / Institutes / Organizations:Fakultät Wirtschaftsingenieurwesen
AImotion Bavaria
Licence (German):License Logo Creative Commons BY-NC-ND 4.0
Release Date:2024/03/25