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In order to overcome the shortcomings that low detection accuracy and slow network training of the real-time object detection algorithm YOLO, this paper proposed an algorithm that added the batch re-normalization to the YOLO network structure. It combined the characteristic of batch re-normalization, which had the strength to deal with small or non-i. i. d. minibatches. The improved algorithm treated the feature maps, which generated from the convolutional layers, as activations, and then batched renormalize the activations, meanwhile it removed the dropout from the original network structure and increased learning rate. The experimental results show that the proposal algorithm has better detection accuracy and faster than before, and furthermore, it can decrease the model training time and the requirement of hardware equipment. [ABSTRACT FROM AUTHOR]