Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education

被引:9
作者
Alhalabi, Marah [1 ]
Ghazal, Mohammed [1 ]
Haneefa, Fasila [1 ]
Yousaf, Jawad [1 ]
El-Baz, Ayman [2 ]
机构
[1] Abu Dhabi Univ, Coll Engn, Elect Comp & Biomed Engn Dept, Abu Dhabi 59911, U Arab Emirates
[2] Univ Louisville, Bioengn Dept, Louisville, KY 40292 USA
关键词
smartphones and learning; engineering education; circuit diagrams; augmented reality; capsule networks; deep learning; Netlist; RECOGNITION; TECHNOLOGY;
D O I
10.3390/educsci11110661
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simulating circuits from hand-drawn diagrams using smartphones through an image recognition system. This method allows students to use their smartphones to capture images instead of creating circuit diagrams before simulation. Our contribution lies in building a circuit recognition system using a deep learning capsule networks algorithm. The developed system receives an image captured by a smartphone that undergoes preprocessing, region proposal, classification, and node detection to get a Netlist and exports it to a circuit simulator program for simulation. We aim to improve engineering education using smartphones by (1) achieving higher accuracy using less training data with capsule networks and (2) developing a comprehensive system that captures hand-drawn circuit diagrams and produces circuit simulation results. We use 400 samples per class and report an accuracy of 96% for stratified 5-fold cross-validation. Through testing, we identify the optimum distance for taking circuit images to be 10 to 20 cm. Our proposed model can identify components of different scales and rotations.
引用
收藏
页数:21
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