Region proposal optimization algorithm based on convolutional neural networks

Region proposals are usually used to efficiently detect objects in object detection. In order to solve the problem that the region proposals have low quality, the convolutional edge features, object saliency and position information of objects are introduced into the region proposals algorithm. Firstly, the edge features with semantically meaningful information are generated from the images to be detected using the convolutional neural networks, and the score of edge information for per sliding window is obtained through the strategy of edge clustering and the similarities between the edge groups. Then, the salient object scores of each sliding window are computed using the local features of salient objects. Thirdly, the scores of object position information are calculated according to the location where objects may occur. Finally, the region proposals are determined by three components including edge information scores, salient object scores and the object positions scores. The experimental results in PASCAL VOC 2007 validation set show that given just 10 000 region proposals, the object recall of the proposed algorithm is 90.50%, that is increased by 3% comparing with Edge Boxes with intersection over union threshold of 0.7. The run time of the proposed method is about 0.76 seconds for processing one image, and this demonstrates that our approach can yield a set of region proposals with higher quality at a fast speed.