An Ultra Lightweight CNN for Low Resource Circuit Component Recognition

TL;DR

This paper introduces an ultra lightweight CNN system designed for recognizing circuit components in images with limited training data, achieving high accuracy and outperforming existing methods.

Contribution

The paper presents a novel two-stage CNN-based approach and releases a new dataset for low-resource circuit component recognition.

Findings

Achieved 93.4% accuracy in circuit component recognition.

Outperformed SVM baseline and RetinaNet solutions.

Demonstrated effectiveness in low-resource settings.

Abstract

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%).