Abstract In micro-operating system, traditional object detection method cannot detect the objects with partial occlusion and multiple poses, and thus an improved faster region convolutional neural network(Faster RCNN) is adopted to solve the problem. On the basis of original Faster RCNN, deep residual network exhibiting excellent performance in image classification is introduced as the framework of the algorithm, and online hard example mining strategy to enhance the performance by alleviating the imbalance between positive and negative examples is employed. The experimental results manifest that the proposed method can detect objects with partial occlusion and multiple poses effectively. The proposed method shows strong adaptability to environment, responds quickly compared with traditional methods, and thus the practicality of it is verified.
Fund:Supported by National High Technology Research and Development Program of China(No.2008AA8041302), National Natural Science Foundation of China(No.60275013)
About author:: PENG Gang(Corresponding author), Ph.D., associate professor. His research interests include robotics and intelligent manufacturing.YANG Shiqi, master student. His research interests include computer vision and deep learning.HUANG Xinhan, bachelor, professor. His research interests include intelligent robot and pattern recognition.SU Hao, master student. His research interests include intelligent robot and deep learning.
PENG Gang,YANG Shiqi,HUANG Xinhan等. Improved Object Detection Method of Micro-operating System Based on Region Convolutional Neural Network[J]. , 2018, 31(2): 142-149.
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2018, Vol. 31 
