Hand-Drawn Electrical Circuit Recognition using Object Detection and Node Recognition

TL;DR

This paper presents a real-time method combining YOLOv5 and Hough transform to recognize hand-drawn electrical circuits, achieving high detection accuracy and schematic reconstruction efficiency.

Contribution

It introduces a novel integrated approach for automatic circuit schematic recognition from hand-drawn images, combining object detection and node recognition techniques.

Findings

Component detection with 98.2% mAP accuracy

Circuit schematic reconstruction with 80% accuracy

Near-real time processing at 0.33 seconds per schematic

Abstract

With the recent developments in neural networks, there has been a resurgence in algorithms for the automatic generation of simulation ready electronic circuits from hand-drawn circuits. However, most of the approaches in literature were confined to classify different types of electrical components and only a few of those methods have shown a way to rebuild the circuit schematic from the scanned image, which is extremely important for further automation of netlist generation. This paper proposes a real-time algorithm for the automatic recognition of hand-drawn electrical circuits based on object detection and circuit node recognition. The proposed approach employs You Only Look Once version 5 (YOLOv5) for detection of circuit components and a novel Hough transform based approach for node recognition. Using YOLOv5 object detection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in detecting the components. The proposed method is also able to rebuild the circuit schematic with 80% accuracy with a near-real time performance of 0.33s per schematic generation.