Jul 20, 2023 · By retraining the model's final layers on a fresh dataset, pre-trained CNN models are capable of being tailored for particular tasks, like PCB defect identification. The results of the trial demonstrated that our technology is suitable for the commercial production of PCBs.

This article aims to showcase practical applications of CNN models for surface defect detection across various industrial scenarios, from pallet racks to display screens. The review explores object detection methodologies and suitable hardware platforms for …

Jan 1, 2022 · Study on models that can be used as detectors for defect detection applications in industry. Study on YOLOv4 that can perform good defect detection with not much capital investment. Analysis on the correlation between dataset, labeling and the data augmentation steps and accuracy and computations.

Feb 27, 2025 · Scanning Acoustic Tomography (SAT) effectively analyzes the internal structures of integrated circuit products, thereby preventing the supply of defective components, including chip fractures, delamination, voids, and adhesion issues.

The recommended approach makes use of CNN for image analysis as well as deep learning-based defect detection for fault detection, including open and short circuits. The production of PCBs is a crucial part of the electronics sector, the reliability and quality of the PCB have a significant impact on its performance.

The paper proposes an improved printed circuit board (PCB) defect detection algorithm based on the original faster region convolutional neural networks (Faster R-CNN) for the problems of low average accuracy mean value, poor detection of tiny defect targets and high leakage rate in PCB tiny defect detection.

These directions include utilizing Transformers as a foundational framework for creating new algorithms and employing techniques like Generative Adversarial Networks (GANs) and reinforcement learning to enhance PCB defect detection performance.

Mar 24, 2025 · Mask R-CNN algorithm demonstrated high accuracy and precision in defect segmentation and detection, offering a promising solution for quality control in PCB manufacturing. The advent of ML allows the development of new solutions for various challenges. For instance, in image classification, ML algorithms can identify objects within optical images.

Jan 16, 2019 · Pre-trained models such as VGG16, DenseNet169 and Inception-V3 are used to investigate which model suits the best for components recognition. Using transfer learning with VGG-16, the...

To automatically detect defects, we use advanced ML models such as Convolutional Neural Networks (CNNs) on a large database of PCB images marked for defects. To provide reliable and accurate detection results, our research focuses on data preparation, feature extraction, model selection, and robust validation.