基于深度学习算法的尿素泵体用铝型材表面瑕疵检测

Surface Flaw Detection of Aluminum Profile for Urea Pump Body Based on Deep Learning Algorithm

尿素泵为机动车尾气后处理系统的核心设备,泵体材料一般为铝型材,在铝型材生产过程中,受工艺等因素的影响会产生各种瑕疵,影响铝型材的质量。传统人工检测,质检的效率和准确率难以满足生产需要。本文将深度学习算法引入到缺陷检测中,结合迁移学习原理,使用小批量数据集,利用改进的YOLO模型进行训练,预测铝型材表面瑕疵。试验结果显示,尽管在小批量训练的条件下,验证集mAP值为87.43%,仍取得了98.2%的准确率,比拟人工检测的准确率,并可以快速、准确的定位缺陷部位。此技术有望革新现有质检流程,自动完成质检任务,保证产品的质量;另外,基于深度学习算法表面缺陷检测方法,鲁棒性好,具有一定的普适性,可以推广到相关的其他应用领域。

Urea pump is the core equipment of vehicle exhaust post-treatment system.Material of pump body is generally aluminum profiles.In the production process of them,due to the influence of process factors,various defects will appear,which will affect the quality of aluminum profiles.Traditional manual detection methods cannot meet the needs of production,because of its efficiency and accuracy.In recent years,deep learning algorithm.The deep learning algorithm was introduced into flaw detection.In combination with transfer learning principle and by using a small-volume dataset,the YOLO model was used for training to predict the defects of aluminum profile.The experimental results show that under the premise of small batch training,the mAP value of verification set was 87.43%,but the accuracy rate of 98.2%was still obtained,which is comparable to the accuracy of manual detection,and the defect can be quickly and accurately located.This technology can be expected to innovate existing quality inspection processes,automatically complete quality inspection tasks and guarantee product quality.In addition,the defect detection method based on deep learning is robust and has certain universality,which can be extended to other related application areas.