自動化AI視覺檢測技術

隨著人工智慧（Artificial Intelligence, AI）與深度學習（Deep Learning）的發展推動視覺檢測從傳統依賴規則的方式，轉型為數據驅動的智能化系統。傳統檢測在光照穩定時具有效率，但對環境變化敏感，易誤判與漏檢。AI 深度學習透過大量標註數據自動提取多層特徵，能適應複雜生產環境，於分類、檢測與分割等任務展現高精度與穩定性。本文介紹 2D 與 3D 視覺技術及其在缺陷檢測、尺寸量測、品質分級的應用，強調其在光照變化容忍度、複雜特徵辨識、即時檢測及自適應優化的優勢[1] [2]。特別是在金屬加工產線，AI 視覺能處理金屬表面反光與隨機堆疊問題，結合工件定位與 3D 視覺辨識，輔助機械手臂完成自動化上、下料，有效提升精度與效率。

The development of artificial intelligence (AI) and deep learning has driven visual inspection to evolve from traditional rule-based methods into data-driven intelligent systems. While conventional inspection performs efficiently under stable lighting conditions, it is highly sensitive to environmental variations, often leading to false detections and missed defects. Through large annotated datasets, AI deep learning enables automatic multi-level feature extraction, allowing adaptation to complex production environments and achieving high accuracy and stability in tasks such as classification, detection, and segmentation. This article introduces 2D and 3D vision technologies and their applications in defect detection, dimensional measurement, and quality grading, emphasizing their advantages in illumination tolerance, complex feature recognition, real-time inspection, and adaptive optimization[1] [2]. In particular, within metal processing production lines, AI vision can address challenges such as reflective surfaces and randomly stacked workpieces. By integrating workpiece localization and 3D vision recognition, the system supports robotic arms in automated loading and unloading operations, significantly improving precision and efficiency.