Recognition of Electronic Component Orientations from Hand-Drawn Circuit Schematics through a Two Stage Machine Learning System
Anuj Mathur, Ramachandra Achar · 2024
Large strides in Artificial Intelligence (AI) and Machine Learning (ML) have lead to the automation of countless manual and time intensive tasks in circuit analysis and simulation. As hand-drawing circuits is often a critical time-consuming step, automating the netlisting and schematic-generation will greatly speedup this manual process. With emerging traction towards AI, research is increasingly being conducted in this field to develop component detection and classifier models. In this paper, a novel two stage detection system with a focus on component accuracy and orientations is developed. Using a hand-drawn circuit dataset of 2304 images by various authors with different drawing styles, an object-detection model was developed using Faster-RCNN, and YOLOv5 to recognize circuit components. Through a custom created dataset of 84 orientation classes for 15 types of electronic components, a secondary classification model was developed using ResNet-50 for the recognition of orientation information with an accuracy of 99.97%.