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Analysis of Subsampled Image Size for Detection and Identification of Brake Pad Contours by Using Deep Learning

机译:利用深度学习分析用于检测和识别制动垫轮廓的夹持图像尺寸

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This paper proposes an analysis of subsampled image size for detection and identification of brake pad contours by using deep learning for the automatic detection systems. In brake pad manufacture, some problems may occur such as expansion, missing corners at the edges so that the edges of the brake pad need to obtain before checking missing corners. The size of subsampled image for training and testing for machine learning is very significant factor for optimized and efficient feature extraction. The size of subsampled image has great impact on detection and identification for feature extraction determines the accuracy of prediction of deep learning. The images are evaluated through loss function in order to observe the training process of the models. The experimental results show the method to determine subsampled image size to have better accuracy.
机译:本文提出了通过利用深度学习对自动检测系统来检测和识别制动垫轮廓的限制图像尺寸的分析。在制动垫制造中,可能发生一些问题,例如膨胀,边缘处的缺失,使得在检查缺失的角落之前需要获得制动垫的边缘。用于训练和测试机器学习的训练图像的大小是优化和有效的特征提取的非常重要的因素。限位图像的大小对特征提取的检测和识别产生了很大的影响,确定了深度学习预测的准确性。通过损耗函数评估图像以观察模型的培训过程。实验结果表明,确定具有更好的精度的离子图像尺寸的方法。

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