Modular network for high accuracy object detection
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We present a novel modular object detection convolutional neural network that significantly improves the accuracy of computer vision object detection. The network consists of two stages in a hierarchical structure. The first stage is a network that detects general classes. The second stage consists of separate networks to refine the classification and localization of each of the general classes objects. Compared to a state of the art object detection networks the classification error in the modular network is improved by approximately 3-5 times, from 12 percent to 2.5-4.5 percent. The modular network achieved a very high score in object detection of 0.94 mAP. The network is easy to implement, it can be a platform to improve the accuracy of widespread state of the art object detection networks and other kinds of deep learning networks.
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