  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%).

Computer Vision and Pattern Recognition