MIXED PCB DEFECT DATASET

This dataset contains images focusing on printed circuit boards (PCBs) and their defects. To maintain uniformity, the images are resized to a standard 640 x 640 pixels. To increase the dataset's realism and relevance for real-time sensor applications, intentional augmentation was applied, adding extra defects to simulate real-world scenarios in PCB manufacturing. Various augmentation techniques were used to diversify the dataset, ensuring better algorithm performance during training and evaluation. The dataset features annotations that precisely label the induced defects, such as missing holes, mouse bites, open circuits, shorts, spurs, and spurious copper issues. With its precise annotations and intentional augmentation, this dataset is a valuable resource for advancing research in real-time PCB defect detection and classification.

If you used the dataset, please cite the following paper:

Kumar Ancha, V., Sibai, F. N., Gonuguntla, V., & Vaddi, R. (2024). Utilizing YOLO Models for Real-World Scenarios: Assessing Novel Mixed Defect Detection Dataset in PCBs. IEEE Access, 12, 100983-100990. https://doi.org/10.1109/ACCESS.2024.3430329