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Open AccessArticle

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

1
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
2
Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
3
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, China
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(1), 93; https://doi.org/10.3390/math8010093
Received: 10 December 2019 / Revised: 21 December 2019 / Accepted: 27 December 2019 / Published: 7 January 2020
(This article belongs to the Special Issue Computing Methods in Steganography and Multimedia Security)
Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces. View Full-Text
Keywords: small scale face; SENet; face detection; SE-IYOLOV3 small scale face; SENet; face detection; SE-IYOLOV3
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MDPI and ACS Style

Deng, Z.; Yang, R.; Lan, R.; Liu, Z.; Luo, X. SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security. Mathematics 2020, 8, 93.

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