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.
彭刚, 杨诗琪, 黄心汉, 苏豪. 改进的基于区域卷积神经网络的微操作系统目标检测方法[J]. 模式识别与人工智能, 2018, 31(2): 142-149.
PENG Gang, YANG Shiqi, HUANG Xinhan, SU Hao. Improved Object Detection Method of Micro-operating System Based on Region Convolutional Neural Network. , 2018, 31(2): 142-149.
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2018, Vol. 31 
