基于Faster R-CNN的办公用品目标检测

RCNN网络与全卷积网络框架等技术使得目标检测技术能够快速发展。RCNN网络与全卷积网络框架不仅训练速度快，推断速度也十分的迅速，此外还具有良好的鲁棒性以及灵活性。在人工智能领域的发展中，提高目标检测效率的关键在于好的技术，以及得到更加有效的、深层的特征表示，通过使用深层网络的多层结构来简洁地表达复杂函数。本文用到的目标检测方法先要用区域建议网络得到建议位置再进行检测，因为Fast R-CNN和R-CNN等目标检测算法已经在运行时间方面有了很大的提高，所以计算区域建议成为目标检测的一个计算瓶颈。本文通过在算法中加入特征融合技术，将每一卷积层提取的特征进行融合，使用区域建议网络来进行候选区域提取。区域建议网络和检测网络共享全图的卷积特征，从而很大程度地缩短候选区域的提取时间，提高目标检测的精度。

Office Supplies Target Detection Based on Faster R-CNN

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.