Abstract Insufficient feature information in object detection results in low accuracy of small targets and occluded targets detection. Therefore, multi-layers context convolutional neural network (MLC-CNN) is proposed, and contextual information of multiple layers is extracted to combine local features of objects in object detection. MLC-CNN consists of region proposal network (RPN) sub-network and multi-layers context (MLC) sub-network. RPN sub-network is employed to capture feature vectors with the fixed length as object features, and MLC is employed to obtain the corresponding contextual information of the different feature maps. Finally, two kinds of information are fused. In addition, hard example training is employed to solve the problem of imbalance data. Experiments on PASCAL VOC2007 and PASCAL VOC2012 datasets indicate that mean average precision (mAP) value is improved.
Fund:Supported by National Natural Science Foundation of China(No.61872327), Natural Science Foundation of Anhui Province(No.1708085MF146), Fundamental Research Funds for the Central Universities(No.ACAIM190102), Project of Innovation Team of Ministry of Education of China(No.IRT17R32)
Corresponding Authors:
FANG Baofu, Ph.D., associate professor. His research interests include machine learning, machine vision and multi-robot systems.
About author:: WANG Hao, Ph.D., professor. His research interests include intelligent computing theory and software, artificial intelligence, machine vision and data mining; SHAN Wenjing, master student. Her research interests include computer application technology, object detection and machine learning.
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2020, Vol. 33 
