A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection

TL;DR

This systematic review compiles and evaluates publicly available datasets for industrial defect detection, highlighting their strengths, limitations, and applicability to aid researchers in benchmarking and model development.

Contribution

It provides a comprehensive, curated overview of 15 datasets from 2015 to 2023, facilitating better dataset selection for defect detection research.

Findings

Diverse datasets with varying defect types and image qualities

Identification of strengths and limitations of each dataset

Consolidation of datasets into a single reference resource

Abstract

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.