Object Recognition with Hybrid Deep Learning Methods and Testing on Embedded Systems

Keywords: Deep learning, Image classification, object detection, hybrid methods

Abstract

Object recognition applications can be made with deep neural networks. However, this process may require intensive processing load. For this purpose, hybrid object recognition algorithms that can be created for the recognition of an object in the image and the comparison of the working time of these algorithms on various embedded systems are emphasized. While Haar Cascade, Local Binary Pattern (LBP) and Histogram Oriented Gradients (HOG) algorithms are used for object detection, Convolutional Neural Network (CNN) and Deep Neural Network (DNN) algorithms are used for classification. As a result, six hybrid structures such as Haar Cascade+CNN, LBP+CNN, HOG+CNN and Haar Cascade+DNN, LBP+DNN, HOG+DNN are developed. In this study, these 6 hybrid algorithms were analyzed in terms of success percentage and time, then compared with each other. Microsoft COCO dataset was used to train and test all these hybrid algorithms. Object recognition success of CNN was 76.33%. Object recognition success of Haar Cascade+CNN, one of the hybrid methods we recommend, with a success rate of 78.6% is higher than CNN and other hybrid methods. LBP+CNN method recognized objects in 0.487 seconds which is faster than any other hybrid methods. In our study, Nvidia Jetson TX2, Asus TinkerBoard, Raspbbery Pi 3 B+ were used as embedded systems. As a result of these tests, Haar Cascade+CNN method on Nvidia Jetson TX2 was detected in 0.1303 seconds, LBP+DNN and Haar Cascade+DNN methods on Asus Tinker Board were detected in 0.2459 seconds, and HOG+DNN method on Raspberry Pi 3 B+ was detected in 0.7153 seconds.

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Published
2020-06-26
How to Cite
[1]
Y. Taspinar and M. Selek, “Object Recognition with Hybrid Deep Learning Methods and Testing on Embedded Systems”, IJISAE, vol. 8, no. 2, pp. 71-77, Jun. 2020.
Section
Research Article