Volume 16 Number 4 (Jul. 2021)
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JSW 2021 Vol.16(4): 157-166 ISSN: 1796-217X
doi: 10.17706/jsw.16.4.157-166

Feature Extraction with Apparent to Semantic Channels for Object Detection

Lei Zhao*, Jia Su, Zhiping Shi, Yong Guan
Information Engineering College, Capital Normal University, Beijing, China.

Abstract—This paper focuses on using traditional image processing algorithms with some apparent-to-semantic features to improve the detection accuracy. Based on the optimization of Faster R-CNN algorithm, a mainstream framework in current object detection scenario, the multi-channel features are achieved by combining traditional image semantic feature algorithms (like Integral Channel Feature (ICF), Histograms of Gradient (HOG), Local Binary Pattern (LBF), etc.) and advanced semantic feature algorithms (like segmentation, heatmap, etc.). In order to realize the joint training of the original image and the above feature extraction algorithms, a unique network for increasing the accuracy of object detection and minimizing system weight called Multi-Channel Feature Network (MCFN) is proposed. The function of MCFN is to provide a multi-channel interface, which is not limited to the RGB component of a single picture, nor to the number of input channels. The experimental result shows the relationship between the number of additional channels, performance of model and accuracy. Compared with the basic Faster R-CNN structure, this result is based on the case of two additional channels. And the universal Mean Average Precision (mAP) can be improved by 2%-3%. When the number of extra channels is increased, the accuracy will not increase linearly. In fact, system performance starts to fluctuate in a range after the number of additional channels reaches six.

Index Terms—Feature, channels, faster R-CNN, semantic.

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Cite: Lei Zhao, Jia Su, Zhiping Shi, Yong Guan, "Feature Extraction with Apparent to Semantic Channels for Object Detection," Journal of Software vol. 16, no. 4, pp. 157-166, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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