# A Comprehensive Framework for Automated Quality Control in the Automotive Industry

arXiv: https://arxiv.org/abs/2512.05579 · HTML: https://mlsanity.com/paper/2512.05579/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Learning non-maximum suppression](https://mlsanity.com/paper/1705.02950/) (2017)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)
- [cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation](https://mlsanity.com/paper/2310.17274/) (2023)
- [Deep Learning Models for Visual Inspection on Automotive Assembling Line](https://mlsanity.com/paper/2007.01857/) (2020)
- [Improved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy](https://mlsanity.com/paper/2311.11439/) (2023)
- [Complex-Object Visual Inspection via Multiple Lighting Configurations](https://mlsanity.com/paper/2004.09374/) (2020)

## Abstract (arXiv)

This paper presents a cutting-edge robotic inspection solution designed to automate quality control in automotive manufacturing. The system integrates a pair of collaborative robots, each equipped with a high-resolution camera-based vision system to accurately detect and localize surface and thread defects in aluminum high-pressure die casting (HPDC) automotive components. In addition, specialized lenses and optimized lighting configurations are employed to ensure consistent and high-quality image acquisition. The YOLO11n deep learning model is utilized, incorporating additional enhancements such as image slicing, ensemble learning, and bounding-box merging to significantly improve performance and minimize false detections. Furthermore, image processing techniques are applied to estimate the extent of the detected defects. Experimental results demonstrate real-time performance with high accuracy across a wide variety of defects, while minimizing false detections. The proposed solution is promising and highly scalable, providing the flexibility to adapt to various production environments and meet the evolving demands of the automotive industry.