  Recent advancements in quality control across various industries have
increasingly utilized the integration of video cameras and image processing for
effective defect detection. A critical barrier to progress is the scarcity of
comprehensive datasets featuring annotated defects, which are essential for
developing and refining automated defect detection models. This systematic
review, spanning from 2015 to 2023, identifies 15 publicly available datasets
and critically examines them to assess their effectiveness and applicability
for benchmarking and model development. Our findings reveal a diverse landscape
of datasets, such as NEU-CLS, NEU-DET, DAGM, KolektorSDD, PCB Defect Dataset,
and the Hollow Cylindrical Defect Detection Dataset, each with unique strengths
and limitations in terms of image quality, defect type representation, and
real-world applicability. The goal of this systematic review is to consolidate
these datasets in a single location, providing researchers who seek such
publicly available resources with a comprehensive reference.
