VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
This provides a benchmark for researchers and practitioners in industrial inspection, though it is incremental as it builds on existing dataset efforts by adding versatility and real-world scenarios.
The authors tackled the lack of diverse and high-quality datasets for vision-based industrial inspection by introducing the VISION Datasets, a collection of 14 datasets with 18k images and 44 defect types, featuring annotation masks and instance-segmentation to support various detection methods.
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.