The NEU-DET dataset is a collection of images featuring surface defects on hot-rolled steel strips. These defects are categorized into six classes: cracks (cr), inclusions (in), patches (pa), pitted surfaces (ps), rolled-in scale (rs), and scratches (sc). The dataset contains 300 grayscale images for each category, totaling 1800 images, with each image sized at 200×200 pixels.

Optical remote sensing images, with their high spatial resolution and wide coverage, have emerged as invaluable tools for landslide analysis. Visual interpretation and manual delimitation of landslide areas in optical remote sensing images by human is labor intensive and inefficient. Automatic delimitation of landslide areas empowered by deep learning methods has drawn tremendous attention in recent years. Mask R-CNN and U-Net are the two most popular deep learning frameworks for image segmentation in computer vision.

This study provides a UAV target dataset for small target UAV detection. The dataset includes 10,800 images taken by high-definition cameras and their annotation files. Each image includes a target drone model DJI Mini 4 Pro. The data set includes a variety of shooting angles and is classified, including 0-30°, 30-60°, and 60-90°. It also includes good daytime lighting conditions and poor evening lighting conditions. According to our experiments, this dataset is sufficiently complex and of high quality.

The dataset is a self-constructed wafer surface defect dataset, with each image captured in real-time. The extraction and segmentation of wafer image have been performed, and each image represents a single individual die. The dataset primarily includes images of defect-free dies, as well as four types of defective images: particle, scratch, stain, and liquid residual. A total of 500 images are included, and the various types of defects within the images have been annotated using the Make Sense online annotation tool.