论文题目: An Adaptive Defect-Aware Attention Network for Accurate PCB-Defect Detection 作者:Xiang Liu 来源:2024 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 摘要:缺陷检测是印刷电路板(PCB)制造过程中质量控制的关键组成部分。然而,准确检测PC
During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the
runs/train/pcb_defect_detection/weights/best.pt: 最佳模型权重。 runs/val/exp/results.txt: 验证结果。 图像输出: runs/detect/exp/sample.jpg: 带有预测边界的图像。 希望这些详细的信息和代码能够帮助你顺利实施和优化你的项目 运行步骤总结 克隆YOLOv5仓库: bash git clone https://github.com/ultralytic...
PCB plug-in solder joint defect detectionYOLOv3Void space convolution poolingFeature information fusionPrinted Circuit Boards (PCBs) are the foundational component of electronic devices, and the detection of PCB defects is essential for ensuring the quality control of electronic products. Aiming at the ...
The algorithmic performance, strengths, and limitations of these methods are compared, and the current challenges in PCB defect detection are summarized. Additionally, this work forecasts future research trends in the field of PCB defect detection. Keywords: PCB / defect detection / machine vision /...
Atthisstage,deeplearningmethodshavebeenwidelyusedinPCBdefectdetection problems,theuseoftargetdetectionmodelcangreatlysavemanpowercostsandimprove productionefficiency.However,therearestillproblemssuchaslackofsampledata,low detectionaccuracy,misseddetection,falsedetection,andunevenmodelaccuracyandsize.In ordertosolvetheprob...
In this paper, the YOLOv7 model is selected as the original model for PCB defect detection. Firstly, the K-means++ clustering algorithm is used to calculate the target anchor parameters which can enhance the dataset. Secondly, the receptive field enhancement (RFE) module is added to the head...
此外,Mask R-CNN通过在Faster R-CNN的基础上添加一个用于生成高质量分割掩码的分支,对于精细缺陷的检测表现出色。近期,基于Transformer的算法如DETR (Detection with Transformers)和ViT (Vision Transformer)在一些研究中被探索用于缺陷检测,尤其是它们在处理复杂图像特征和关系时显示出的潜力。
PCB defect detection based on pattern matching and segmentation algorithmThis project presents a new implementation which separates two of the existing groups containing two defects each into four new groups containing one defect each by processing synthetic images ...
Gao et al. (2019). "Automatic PCB Defect Detection Using Deep Learning and Unsupervised Feature Learning" 该研究提出了一种基于深度学习的PCB缺陷检测方法,利用卷积神经网络(CNN)自动提取特征,并结合无监督学习方式对电路板缺陷进行检测。该方法不依赖于传统的人工特征工程,具有较强的鲁棒性和较高的检测精度。