目标检测
计算机科学
帧(网络)
帧速率
人工智能
计算机视觉
机器视觉
对象(语法)
实时计算
模式识别(心理学)
电信
作者
Su Kuan-Ying,Mingfei Chen,Tsai Po-Cheng,Tsai Cheng-Han
标识
DOI:10.1109/iet-iceta56553.2022.9971568
摘要
The purpose of this research is to develop a real-time bicycle frame's defect detection system using YOLO (You Only Look Once) and machine vision. Firstly, the defect locations are manually selected and a database is established. Next, a Darknet method is used to train the YOLO model. Its static detection accuracy rate is 92.6%, and then the static training model is combined with a robotic arm and an industrial camera to perform dynamic detection verification. The result shows that its detection rate reaches 87%. Finally, the above-mentioned defect detection technology is used with the detection machine to complete the development of the online defect detection system.
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