Deep Learning Models for Visual Inspection on Automotive Assembling Line

TL;DR

This paper explores deep learning techniques to enhance visual inspection in automotive manufacturing, aiming to improve accuracy and ease of setup while minimizing environmental impact and operational disruption.

Contribution

It introduces a deep learning-based framework for visual inspection tasks, demonstrated through four real-world automotive assembly line applications.

Findings

Improved inspection accuracy with deep learning models.

Reduced setup complexity for computer vision systems.

Successful deployment in real manufacturing environments.

Abstract

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 quality of image acquisition. These issues entail costly solutions and override part of the benefits introduced by computer vision systems, mainly when it interferes with the operating cycle time of the factory. In this sense, this paper proposes the use of deep learning-based methodologies to assist in visual inspection tasks while leaving very little footprints in the manufacturing environment and exploring it as an end-to-end tool to ease CVSs setup. The proposed approach is illustrated by four proofs of concept in a real automotive assembly line based on models for object detection, semantic segmentation, and anomaly detection.