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In this paper, we present an ultra lightweight system that can effectivelyrecognize different circuit components in an image with very limited trainingdata. Along with the system, we also release the data set we created for thetask. A two-stage approach is employed by our system. Selective search wasapplied to find the location of each circuit component. Based on its result, wecrop the original image into smaller pieces. The pieces are then fed to theConvolutional Neural Network (CNN) for classification to identify each circuitcomponent. It is of engineering significance and works well in circuitcomponent recognition in a low resource setting. The accuracy of our systemreaches 93.4\%, outperforming the support vector machine (SVM) baseline(75.00%) and the existing state-of-the-art RetinaNet solutions (92.80%).

An Ultra Lightweight CNN for Low Resource Circuit Component Recognition