defect detection
Due to the lack of publicly available injection-molded product defect datasets and the diversity of defects in terms of shapes, sizes, and textures, we collects defect samples from injection molding factories to ensure the model performs well in real industrial scenarios. To ensure the quality and usability of the data, after analyzing the sample data, data cleaning is performed to remove the irregular images.
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The steel tube dataset comprises comprehensive information on various attributes related to steel tubes, encompassing dimensions, material composition, manufacturing processes, and performance characteristics. This dataset facilitates in-depth analysis of steel tube properties, aiding researchers, engineers, and industry professionals in optimizing designs, ensuring structural integrity, and advancing materials science in the context of steel tube applications.
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It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the fifinal product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classifification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some diffificulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects.
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This dataset collects samples of different kinds of defective and normal chenille yarn images for the same batch of chenille yarn made of polyester material, aiming to facilitate the task of recognizing and classifying chenille yarn defects in computer vision and machine learning algorithms. This dataset consists of a total of 2500 images of 5 major chenille yarn defects and 2500 normal chenille yarn images, totaling 5000 images. It is captured by an industrial camera in the state of chenille yarn movement.
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Dataset for segmentation of the defects on the surfaces of the military cartridge cases. The datasets with non-defective, defective and masked image classes of the defective cartridge cases.
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The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD) dataset for polycrystalline solar cell, which contains 36,543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly-free images and anomalous images with 10 different categories.
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