In terms of CNN-based methods, Wu et al. propose to use mask R-CNN method for defect joint localization and detection. Cai et al. propose to use three cascaded levels of CNNs for solder joint defect detection. As the number of levels increase, the performance gets better.
Recent advancements in production processes and technology have led to PCBs becoming more miniaturized and complex, thereby increasing the likelihood of soldering defects such as leakage welding, less tin, even tin, tip, hole, and other defects. Therefore, it is necessary to detect defects in PCB solder joints.
Each image is uniquely identified by the image path. In the mega dataset, it consists of information, such as image path, board number, slice number, joint type, machine defect, ROI, label, etc. Each solder joint can be uniquely identified by the image path together with the ROI.
However, existing work rarely involves defect detection for PCBs based on 3D point clouds. In this paper, we propose a novel neural network named double-flow region attention network (DoubRAN) to detect defects of solder joints with 3D point clouds.
Abstract: Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized.
In this paper, we design a new defect detection system to detect defects of solder joints with 3D point clouds. First, we design a binocular lidar system to solve the problem that the monocular lidar system cannot acquire point clouds efficiently.
In this work, we outline a trustworthy, explainable deep learning-driven solder joint inspection system for electronics manufacturing. The proposed system is capable of handling both …
This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions ...
We proposed to use the deep learning method YOLO to locate hundreds of small and closely spaced solder joints in PCB images automatically. Our experiment shows that it …
This paper proposes to detect solder joint defects with machine learning methods using YOLO algorithm to speed up time and increase accuracy in assembly PCB production line. …
This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination ...
We propose YOLOv2 network with the feature extraction of ResNet-50 to train the models to detect the solder joint defects. The accuracy of the models achieved 88.56% for the good …
This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different …
AbstractThis paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). …
A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection …
Using YOLOv3 network as the basic framework, this paper proposes a PCB plug-in solder joint defect detection method based on spatial convolutional pooling and …
The proposed solder joint quality detection system for aviation plugs is shown in Figure 3 . It is mainly composed of two parts: data acquisition and detection approach.
Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, …
The solder joint defect detection system based on computer vision has the characteristics of real-time, continuous, and contact-less. ... We combine a coordinate …
: failure criterion, solder joint, interconnection, reliability, control chart . 1. Introduction. One of the challenges in an experimental study of solder joint reliability is to determine when cracks occur …
Purpose The authors propose a solder joint recognition method based on eigenspace technology. Design/methodology/approach The original solder joint image is …
In this paper, we design a new defect detection system to detect defects of solder joints with 3D point clouds. First, we design a binocular lidar system to solve the problem that …
Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, …
In this work, we outline a trustworthy, explainable deep learning-driven solder joint inspection system for electronics manufacturing. The proposed system is capable of handling both …
Cracks in a real solder joint are difficult to identify using an X-Ray system. Cross-sectioning and scanning electron microscopy (SEM) is a destructive method. A common non-destructive test
This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both …
Four joint defect detection models based on artificial intelligence are proposed and compared and the effectiveness of the proposed models is verified by experiments on real …
In the mega dataset, it consists of information, such as image path, board number, slice number, joint type, machine defect, ROI, label, etc. Each solder joint can be …