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Sensors 2018, 18(9), 3041; https://doi.org/10.3390/s18093041

Statistic Experience Based Adaptive One-Shot Detector (EAO) for Camera Sensing System

1
Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Received: 22 July 2018 / Revised: 7 September 2018 / Accepted: 8 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Semantic Representations for Behavior Analysis in Robotic system)
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Abstract

Object detection in a camera sensing system has been addressed by researchers in the field of image processing. Highly-developed techniques provide researchers with great opportunities to recognize objects by applying different algorithms. This paper proposes an object recognition model, named Statistic Experience-based Adaptive One-shot Detector (EAO), based on convolutional neural network. The proposed model makes use of spectral clustering to make detection dataset, generates prior boxes for object bounding and assigns prior boxes based on multi-resolution. The model is constructed and trained for improving the detection precision and the processing speed. Experiments are conducted on classical images datasets while the results demonstrate the superiority of EAO in terms of effectiveness and efficiency. Working performance of the EAO is verified by comparing it to several state-of-the-art approaches, which makes it a promising method for the development of the camera sensing technique. View Full-Text
Keywords: remote sensing; convolutional neural network; image recognition; object detection; prior boxes remote sensing; convolutional neural network; image recognition; object detection; prior boxes
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Zhu, X.; Ding, B.; Meng, Q.; Gu, L.; Yang, Y. Statistic Experience Based Adaptive One-Shot Detector (EAO) for Camera Sensing System. Sensors 2018, 18, 3041.

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