# SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

arXiv: https://arxiv.org/abs/2604.26633 · HTML: https://mlsanity.com/paper/2604.26633/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Synthetic Data from Diffusion Models Improves ImageNet Classification](https://mlsanity.com/paper/2304.08466/) (2023)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation](https://mlsanity.com/paper/1804.06534/) (2018)
- [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://mlsanity.com/paper/2407.17140/) (2024)
- [Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion](https://mlsanity.com/paper/2302.03298/) (2023)
- [YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection](https://mlsanity.com/paper/2509.25164/) (2025)
- [RF-DETR: Neural Architecture Search for Real-Time Detection Transformers](https://mlsanity.com/paper/2511.09554/) (2025)
- [LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection](https://mlsanity.com/paper/2406.03459/) (2024)
- [DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection](https://mlsanity.com/paper/2503.13985/) (2025)
- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [6D Strawberry Pose Estimation: Real-time and Edge AI Solutions Using Purely Synthetic Training Data](https://mlsanity.com/paper/2511.11307/) (2025)

## Abstract (arXiv)

Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly. We present SynSur, an end-to-end pipeline for synthetic defect generation and automatic annotation, designed to reduce the manual effort and data scarcity that limit deployed inspection systems. The pipeline combines Vision-Language-Model-based prompt construction, LoRA-adapted diffusion, mask-guided inpainting, metric-based sample filtering, and automatic label derivation. We evaluate SynSur on BSData (pitting defects on ball screw drives) and the scratch subset of MSD, reporting downstream detection performance across YOLOX, YOLOv26, and LW-DETR under real-only, synthetic-only, mixed, and union training regimes. The full generation pipeline processes a candidate pool of 1,000 images and delivers a filtered, annotated synthetic dataset with no manual labeling effort. Synthetic-only training does not replace real data; however, augmenting the full real set with synthetic samples yields consistent AP gains in selected configurations, and augmenting scarce real sets substantially reduces the performance gap to full real-data training. The cross-domain study on MSD confirms pipeline portability while highlighting the importance of domain-specific adaptation and annotation quality control. SynSur demonstrates that diffusion-based defect synthesis is a practically viable tool for strengthening industrial inspection pipelines, particularly where labeled data collection is the primary bottleneck.