Microcontroller Boards Damage Detection Dataset

Description

This dataset is a specialized annotated image dataset developed for microcontroller board damage detection using computer vision and deep learning. The dataset contains 7,169 images across 14 classes, covering both good-condition microcontroller boards and defective components. The classes include ATmega2560_Damage, ATmega328_Damage, Board_Damage, Boot_Button_Damage, Capacitor_Damage, Good_Arduino_Mega, Good_Arduino_Nano, Good_Arduino_Uno, Good_ESP32, Pin_Damage, Power_Port_Damage, Reset_Button_Damage, USB-B_Port_Damage, and USB_Input_Damage. The images represent real-world microcontroller board conditions with variations in illumination, board orientation, background, and image quality. All images are standardized to a resolution of 640 × 640 pixels and are provided with YOLO-compatible object-detection annotations. The dataset is organized into training, validation, and testing subsets. The dataset was prepared using Roboflow, including image preprocessing and augmentation applied to the training subset. The final dataset contains 6,273 training images, 597 validation images, and 299 test images. The training-set augmentation was performed after the original dataset split, with the original dataset containing 2,987 images before training-set augmentation. The dataset is provided to support reproducible research in microcontroller defect detection, object detection, automated visual inspection, and computer vision-based quality assessment. A metadata file, metadata_MicrocontrollerBoard.csv, is also provided to facilitate image-level analysis, dataset exploration, and further reuse of the dataset.

Institutions

• United International University

  Dhaka Division, Dhaka

Categories

Licence