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Open AccessFeature PaperArticle

Efficient Evolutionary Learning Algorithm for Real-Time Embedded Vision Applications

by 1, 2 and 1,2,*
1
School of Electrical and Computer Engineering, Nanfang College of Sun Yat-sen University, Guangzhou 510970, China
2
Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, USA
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1367; https://doi.org/10.3390/electronics8111367
Received: 26 October 2019 / Revised: 13 November 2019 / Accepted: 16 November 2019 / Published: 18 November 2019
(This article belongs to the Section Computer Science & Engineering)
This paper reports the development of an efficient evolutionary learning algorithm designed specifically for real-time embedded visual inspection applications. The proposed evolutionary learning algorithm constructs image features as a series of image transforms for image classification and is suitable for resource-limited systems. This algorithm requires only a small number of images and time for training. It does not depend on handcrafted features or manual tuning of parameters and is generalized to be versatile for visual inspection applications. This allows the system to be configured on the fly for different applications and by an operator without extensive experience. An embedded vision system, equipped with an ARM processor running Linux, is capable of performing at roughly one hundred 640 × 480 frames per second which is more than adequate for real-time visual inspection applications. As example applications, three image datasets were created to test the performance of this algorithm. The first dataset was used to demonstrate the suitability of the algorithm for visual inspection automation applications. This experiment combined two applications to make it a more challenging test. One application was for separating fertilized and unfertilized eggs. The other one was for detecting two common defects on the eggshell. Two other datasets were created for road condition classification and pavement quality evaluation. The proposed algorithm was 100% for fertilized egg detection and 98.6% for eggshell quality inspection for a combined 99.1% accuracy. It had an accuracy of 92% for the road condition classification and 100% for pavement quality evaluation. View Full-Text
Keywords: evolutionary learning; embedded vision sensor; visual inspection; egg quality inspection; road condition detection; pavement quality evaluation evolutionary learning; embedded vision sensor; visual inspection; egg quality inspection; road condition detection; pavement quality evaluation
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MDPI and ACS Style

Guo, Z.; Zhang, M.; Lee, D.-J. Efficient Evolutionary Learning Algorithm for Real-Time Embedded Vision Applications. Electronics 2019, 8, 1367. https://doi.org/10.3390/electronics8111367

AMA Style

Guo Z, Zhang M, Lee D-J. Efficient Evolutionary Learning Algorithm for Real-Time Embedded Vision Applications. Electronics. 2019; 8(11):1367. https://doi.org/10.3390/electronics8111367

Chicago/Turabian Style

Guo, Zhonghua; Zhang, Meng; Lee, Dah-Jye. 2019. "Efficient Evolutionary Learning Algorithm for Real-Time Embedded Vision Applications" Electronics 8, no. 11: 1367. https://doi.org/10.3390/electronics8111367

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