Industrial Printed Circuit Board Surface Defect Dataset for Object Detection

Surface defect detection of printed circuit boards (PCBs) is critical for quality control in electronic manufacturing. Existing public datasets often rely on synthetic images or are collected under controlled laboratory conditions, and may not fully capture the complex illumination variations and large-scale defect diversity present in real production lines. Here, this Data Descriptor presents PCB-IND, a large-scale real-world dataset directly acquired from an industrial automated optical inspection (AOI) system. The dataset contains 4,789 images covering eight typical surface defect categories common in industrial inspection, with a total of 5,932 annotated defect instances. Compared to existing benchmarks, PCB-IND preserves non-uniform illumination, high-contrast imaging characteristics typical of etching inspection, and cross-scale defect patterns found in industrial environments. It also differs from prior PCB datasets by combining inline AOI acquisition, natural long-tail defect distribution, and standardized multi-format release within a single industrial dataset resource. To evaluate the usability and benchmark stability of the dataset, we conducted experiments using several representative object detection models. The results indicate that PCB-IND supports the training and evaluation of various models under real industrial conditions, providing a useful real-world data resource for research in industrial defect detection, early-stage process monitoring, and defect repair decision-making.