An Object Detection Algorithm for Deep Learning Based on Batch Normalization.

Based on the advantage of deep learning in object extraction, in this paper we design a deep network that adds Batch-Normalization to the convolution layer. Batch-Normalization has three main advantages. Firstly, it normalizes the input data, which can speed up the fitting of parameters. Secondly, Batch-Normalization can reconstruct the distribution of the input data, so that the feature of input data will not be lost. Thirdly, Batch-Normalization is able to prevent over-fitting, so it can replace Dropout, Local Response Normalization to simplify the network. The network in this paper adopted region proposal to get region of interests. Training classification and position adjustment at the same time to improve accuracy. Comprehensive experimental results have demonstrated the efficacy of the proposed network for objects detection.