# DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries

arXiv: https://arxiv.org/abs/2311.03725 · HTML: https://mlsanity.com/paper/2311.03725/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing](https://mlsanity.com/paper/1708.01894/) (2017)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review](https://mlsanity.com/paper/2505.00210/) (2025)
- [Generative Model Predictive Control in Manufacturing Processes: A Review](https://mlsanity.com/paper/2511.17865/) (2025)
- [Multi-Task Deep Learning for Surface Metrology](https://mlsanity.com/paper/2510.20339/) (2025)

## Abstract (arXiv)

Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely identifying faults by extracting intricate details from product photographs, utilizing RNNs to detect evolving errors and generating synthetic defect data to bolster the model's robustness and adaptability across various defect scenarios. The project leverages a deep learning framework to automate real-time flaw detection in the manufacturing process. It harnesses extensive datasets of annotated images to discern complex defect patterns. This integrated system seamlessly fits into production workflows, thereby boosting efficiency and elevating product quality. As a result, it reduces waste and operational costs, ultimately enhancing market competitiveness.