A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects
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Abstract (arXiv)
Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 179 of its 298 references that are in the map. First posted on arXiv 15 Jul 2025.
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179 of 298 references.
References · 179
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2023Segment Anythingcited by 8,234
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
- 2015Fast R-CNNcited by 2,933
- 2015Variational Inference with Normalizing Flowscited by 2,170
- 2020Point Transformercited by 1,470
38 more
- 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 748
- 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
- 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
- 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
- 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
- 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
- 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
- 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
- 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
- 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
- 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
- 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
- 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
- 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
- 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
- 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
- 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
- 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
- 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
- 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
- 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
- 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 232
- 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
- 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
- 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
- 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 179
- 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
- 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
- 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 167
- 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
- 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
- 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
- 2016Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registrationcited by 159
- 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 154
- 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
- 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
- 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
- 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
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