Abstract: The technologies of RCNN network and full convolution network framework enable target detection technology to develop rapidly. RCNN networks and full convolution network frameworks are not only fast in training, but also very fast in inference speed. Besides, it has good robustness and flexibility. In the development of artificial intelligence, the key to improve the efficiency of target detection lies in good technology. We need to get a more effective and deep feature representation, which can express complex functions simply by using the multi-layer structure of deep network. The target detection method used in this paper is first to use the regional recommended network to get the proposed location and then test, because the Fast R-CNN and R-CNN target detection algorithms have been greatly improved in the running time, so the calculation area is suggested to be a computing bottle neck of the target detection. This paper joins the feature fusion technology in the algorithm, combines the features extracted from each layer of the accumulated layer, uses the regional recommended network to extract the candidate region.The regional recommendation network and the detection network share the convolution feature of the whole graph, so that the extraction time of the candidate region can be greatly shortened and the accuracy of target detection is improved.

Key words: target detection, full convolutional network, regional proposal network