论文标题
PCB缺陷检测使用降级卷积自动编码器
PCB Defect Detection Using Denoising Convolutional Autoencoders
论文作者
论文摘要
印刷电路板（PCB）是制造电子产品的最重要阶段之一。 PCB的小缺陷可能会在最终产品中引起重大缺陷。因此，检测PCB中的所有缺陷并找到它们是必不可少的。在本文中，我们提出了一种基于降级的卷积自动编码器，用于检测有缺陷的PCB并定位缺陷。 DeNoising自动编码器拍摄损坏的图像，并尝试恢复完整的图像。我们用有缺陷的PCB训练了模型，并强迫它修理有缺陷的零件。我们的模型不仅检测到各种缺陷并找到它们，而且还可以修复它们。通过从输入中减去修复后的输出，有缺陷的零件。实验结果表明，我们的模型以高精度（97.5％）的状态检测到有缺陷的PCB与最新作品相比。
Printed Circuit boards (PCBs) are one of the most important stages in making electronic products. A small defect in PCBs can cause significant flaws in the final product. Hence, detecting all defects in PCBs and locating them is essential. In this paper, we propose an approach based on denoising convolutional autoencoders for detecting defective PCBs and to locate the defects. Denoising autoencoders take a corrupted image and try to recover the intact image. We trained our model with defective PCBs and forced it to repair the defective parts. Our model not only detects all kinds of defects and locates them, but it can also repair them as well. By subtracting the repaired output from the input, the defective parts are located. The experimental results indicate that our model detects the defective PCBs with high accuracy (97.5%) compare to state of the art works.
