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Evaluation of Industrial Based Object Detection Method Using Artificial Neural Network

F. S. Ishaq, I. A. Alhaji, Halis Altun,Y. Atomsa, M. L. Jibrin, S. A. Sani, ,

Affiliations
Dept. of Mathematics and Computer Science, Faculty of Science, Federal University, Kashere, Gombe, Nigeria

:10.22362/ijcert/2018/v5/i2/v5i207

Abstract

The essence of the study is to analyse an algorithm which will provide a robust and computationally light method, which might be suitable to implement in the real-time industrial application such as object detection and recognition. For industrial applications, the primary step in automatic detection and classification of an object is to find the object automatically from an image using features related to its shape. This chore is a very complex one. Therefore, to hit the target Histogram of oriented gradient (HOG) algorithm is selected to extract the image features. Average Magnitude Difference Function AMDF is employed to correct the alignment defect. Finally, Artificial Neural Network (ANN) was employed to detect the type of object in the image efficiently. None the less, a database was generated. The database consists of images of real industrial products which are of different shapes and sizes, captured under different lightning conditions. The outcome of the experiment conducted on the database recorded 98.10% success.

Citation

F. S. Ishaq, I. A. Alhaji,Halis Altun,Y. Atomsa, M. L. Jibrin, S. A. Sani(2018). Evaluation of Industrial Based Object Detection Method Using Artificial Neural Network. International Journal of Computer Engineering In Research Trends, 5(2), 50-55. Retrieved from http://ijcert.org/ems/ijcert_papers/V5I207.pdf

Keywords : 1-D mask, HOG algorithm, AMDF algorithm, k-nearest Neighbours algorithm, Cross-correlation Functions algorithms and MLP algorithm

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DOI Link : https://doi.org/10.22362/ijcert/2018/v5/i2/v5i207

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Citations Indices	All
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h-index	14
i10-index	20
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