DeepPCB

DeepPCB: a dataset contains 1,500 image pairs, each of which consists of a defect-free template image and an aligned tested image with annotations including positions of 6 most common types of PCB defects: open, short, mousebite, spur, pin hole and spurious copper. All the images in this dataset are obtained from a linear scan CCD in resolution around 48 pixels per 1 millimetre. The defect-free template images are manually checked and cleaned from sampled images in the above manner. The original size of the template and tested image is around 16k x 16k pixels. Then they are cropped into many sub-images with size of 640 x 640 and aligned through template matching techniques. Next, a threshold is carefully selected to employ binarization to avoid illumination disturbance. Notice that pre-processing algorithms can be various according to the specific PCB defect detection algorithms, however, the image registration and thresholding techniques are common process for high-accuracy PCB defect localization and classification. An example pair in DeepPCB dataset is illustrated in the following figure, where the right one is the defect-free template image and the left one is the defective tested image with the ground truth annotations.

DeepPCB数据集包含1500组图像对，每组图像对均由一张无缺陷模板图像与一张对齐后的待测图像组成，并附带6种最常见PCB缺陷的位置标注：开路（open）、短路（short）、咬边（mousebite）、毛刺（spur）、针孔（pin hole）与杂铜（spurious copper）。本数据集所有图像均由线扫描CCD（linear scan CCD）采集，分辨率约为每1毫米48个像素。无缺陷模板图像均经人工核验，并通过上述方式从采样图像中提纯得到。模板图像与待测图像的原始尺寸约为16k×16k像素，随后被裁剪为大量640×640像素的子图像，并通过模板匹配（template matching）技术完成对齐。随后会精心选取阈值进行二值化（binarization）处理，以规避光照干扰。需注意：预处理算法可根据具体的PCB缺陷检测算法灵活调整，但图像配准与阈值化技术是实现高精度PCB缺陷定位与分类的通用流程。下图展示了DeepPCB数据集中的一组示例图像对：右侧为无缺陷模板图像，左侧为带有真实标注（ground truth annotations）的带缺陷待测图像。

搜集汇总

背景与挑战

背景概述

DeepPCB是一个专门用于印刷电路板（PCB）缺陷检测的数据集，包含1500对高分辨率图像（每对包括无缺陷模板图像和带缺陷的测试图像），并标注了6种常见缺陷类型。图像经过对齐、裁剪和二值化等预处理，适用于高精度的PCB缺陷定位和分类算法开发。