An object recognition method based on deep BCNN with Reinforced Dense Blocks

In the computer vision field, object recognition becomes a very active field of interest. In this research, we proposed a very deep learning approach namely Boosted DenseNet. Our method used the BCNN architecture boosted by conducting reinforced dense blocks. These reinforced dense blocks consisted of a similar convolutional layers numbers as BCNN boosted by adding MBA Activation and Concatenated Rectified Linear Unit functions. In addition, our Boosted DenseNet was improved by applying boosted convolutional layers which offer a deeper network with the same number of parameters. Besides, our method has been reinforced by adding Generalizing Pooling layer. Generalizing pooling, which combined pooling operations within a hierarchical tree structure, replaced the max-pooling layer in reinforced dense blocks. Experiments on CFAR-10 and Pascal VOC 2007 have outperformed the state-of-the-art approaches and demonstrated the robustness of our model for object recognition tasks.