Analysis of Training Deep Learning Models for PCB Defect Detection.

Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection stably. To this end, we first summarize the characteristics of industrial images, such as PCB images. Then, the factors that can cause changes (contamination and quality degradation) to the image data in the industrial field are analyzed. Subsequently, we organize defect detection methods that can be applied according to the situation and purpose of PCB defect detection. In addition, we review the characteristics of each method in detail. Our experimental results demonstrated the impact of various degradation factors, such as defect detection methods, data quality, and image contamination. Based on our overview of PCB defect detection and experiment results, we present knowledge and guidelines for correct PCB defect detection.

Product Inspection Methodology via Deep Learning: An Overview.

In this study, 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. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

PaMM: Pose-Aware Multi-Shot Matching for Improving Person Re-Identification.

Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching. It robustly estimates individual poses and efficiently performs multi-shot matching based on the pose information. The experimental results obtained by using public person re-identification data sets show that the proposed methods outperform the current state-of-the-art methods, and are promising for accomplishing person re-identification under diverse viewpoints and pose variances.