用机器人+AI自动检测汽车铸件缺陷，精度高且可扩展。
A Comprehensive Framework for Automated Quality Control in the Automotive Industry
- 双机械臂配高清摄像头，结合优化光照与镜头捕获图像。
- 基于YOLO11n模型，通过切片、集成学习等提升检测准确率。
- 能实时识别表面和螺纹缺陷，适合各类汽车生产场景。
本文提出一种先进的机器人检测方案，用于自动化汽车制造中的质量控制。系统由一对协作机器人组成，每台配备高分辨率相机视觉系统，精准检测铝合金高压铸造（HPDC）汽车零部件的表面和螺纹缺陷。通过专用镜头和优化照明配置，确保图像采集的一致性与高质量。采用改进的YOLO11n深度学习模型，融合图像切片、集成学习及边界框合并技术，显著提升性能并减少误检。同时结合图像处理技术评估缺陷程度。实验表明，该方案具备实时性与高准确性，在多种缺陷类型下表现优异，且误检率低。该解决方案具有高度可扩展性，可灵活适配不同生产环境，满足汽车行业持续演进的需求。
原文摘要 · Abstract (English)
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.
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