基于机器视觉的大尺寸集装箱门板测量技术研究OA

To address the contradiction between accuracy and efficiency in the large-size detection of container door panels,an online visual measurement method integrating AI and traditional machine vision is proposed.To achieve this goal,an online measurement platform for the dimensions of container door panels is established,and the corresponding image dataset is enriched.In terms of the methodology,the YOLOv10 model is adopted for rapid and preliminary identification and localization of door panel corner points.Traditional machine vision techniques are combined to accurately determine the corner coordinates,and the actual dimensions of the door panels are obtained via a precise scale conversion algorithm.The entire measurement process significantly improves detection efficiency while ensuring measurement accuracy.Experimental results show that compared with previous technologies,the proposed method greatly reduces the computational load of image processing while achieving high-precision detection,with the average detection error controlled within 5 mm.This achievement not only provides an accurate and efficient technical solution for the online large-size measurement of container door panels,but also further promotes the expansion and innovation of related applications in smart factories,thus demonstrating enormous potential and application value in practical industrial production.