论文标题
低资源电路组件识别的超轻型CNN
An Ultra Lightweight CNN for Low Resource Circuit Component Recognition
论文作者
论文摘要
在本文中，我们提出了一个超轻型系统，该系统可以有效地识别图像中具有非常有限的训练数据的不同电路组件。与系统一起，我们还发布了为任务创建的数据集。我们的系统采用了两阶段的方法。应用选择性搜索以找到每个电路组件的位置。根据其结果，我们将原始图像裁剪成小块。然后将这些零件馈入卷积神经网络（CNN）进行分类以识别每个电路组件。它具有工程意义，并且在低资源设置中的电路组件识别中效果很好。我们系统的准确性达到93.4 \％，超过了支持向量机（SVM）基线（75.00％）和现有的最新视网膜解决方案（92.80％）。
In this paper, we present an ultra lightweight system that can effectively recognize different circuit components in an image with very limited training data. Along with the system, we also release the data set we created for the task. A two-stage approach is employed by our system. Selective search was applied to find the location of each circuit component. Based on its result, we crop the original image into smaller pieces. The pieces are then fed to the Convolutional Neural Network (CNN) for classification to identify each circuit component. It is of engineering significance and works well in circuit component recognition in a low resource setting. The accuracy of our system reaches 93.4\%, outperforming the support vector machine (SVM) baseline (75.00%) and the existing state-of-the-art RetinaNet solutions (92.80%).
