Product Inspection Methodology via Deep Learning: An Overview

TL;DR

This paper provides a comprehensive overview of deep learning-based product inspection systems, detailing models, connection schemes, maintenance methods, and performance evaluation in various scenarios.

Contribution

It introduces a unified framework for deep learning-based product inspection, including connection schemes and maintenance strategies, with detailed explanations and performance analysis.

Findings

The proposed system shows good stability and maintenance capabilities.

Performance varies across different test scenarios, demonstrating robustness.

The framework effectively integrates multiple deep learning models for inspection.

Abstract

In this work, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. Also we explain entire steps for building a deep learning-based inspection system in great detail. Second, we address connection schemes that efficiently link the deep learning models to the product inspection systems. Finally, we propose an effective method that can maintain and enhance the deep learning models of the product inspection system. It has good system maintenance and stability due to the proposed methods. All the proposed methods are integrated in a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compared and analyzed the performance of methods in various test scenarios.