快速特征金字塔和Soft-Cascade在折角塞门图像故障检测中的应用  

Application of Fast Feature Pyramids and Soft-Cascade in Image Fault Detection for Angle Cock

作　　者：孙国栋[1] 林凯 高媛 张杨 赵大兴[1] Sun Guodong;Lin Kai;Gao Yuan;Zhang Yang;Zhao Daxing(School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China;Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China)

机构地区：[1]湖北工业大学机械工程学院,武汉430068 [2]南京大学计算机科学与技术系,南京210023

出　　处：《机械科学与技术》2019年第6期947-952,共6页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金项目(51775177,51675166);江苏省自然科学基金项目(BK20150016)资助

摘　　要：为了提升列车折角塞门的故障检测效率,提出了一种基于快速特征金字塔和Soft-Cascade的故障图像检测算法。首先,构建快速特征金字塔模型来提取图像多尺度聚合通道特征;其次,利用向量化后的多尺度聚合通道特征来训练Soft-Cascade故障分类器;最后,利用训练好的分类器来判断待检折角塞门是否含有故障。实验结果表明:该算法的故障检测正确率为97.33%,离线检测速度高达43fps(每张图像仅需23ms),检测效率高于其他算法。该算法训练时间短,检测速度快,硬件要求低,能满足列车折角塞门的故障检测要求。The fault detection algorithm based on the fast feature pyramids and Soft-Cascade was proposed in order to improve the efficiency of the fault detection for the angled cock. Firstly, the fast feature pyramids model was constructed to extract the image multi-scale aggregate channel features. Secondly, the vectorized multi-scale aggregate channel features was used to train the Soft-Cascade fault classifier. Finally, the trained classifier was used to detect whether the angle cock contains a fault. The experimental results show that the fault detection accuracy of the proposed algorithm is of 97.33%, the offline detection speed is up to 43 fps (only 23 ms per image), and the detection efficiency is higher than that by using other algorithms. The present algorithm has the short training time, fast detection speed and low hardware requirements, which can meet the requirements of fault detection for the angle cock.

关 键 词：机器视觉 折角塞门 快速特征金字塔 Soft-Cascade算法 

分 类 号：TN911.73[电子电信—通信与信息系统]