In order to solve the low recall problem of the region proposals in object detection, the object region proposals algorithm, which combines neural networks and superpixels, was proposed. The edge features, which can be represented clearly by neural networks, were extracted from the images to be detected, and the score of edge information for per sliding window was computed by the strategy of edge clustering and the affinities between the edge groups. The several superpixels of this images were obtained by simple linear iterative clustering algorithm, and the salient object score of a superpixel was calculated using the location, integrity of this superpixel and the contrast with neighbors. The salient objects score of per sliding window was received by these saliency scores of superpixels according to the Euler distance strategy between the sliding window and these superpixels. The region proposals were determined by two components including edge information scores and salient object scores. The comparative experiments were conducted in PASCAL VOC 2007 test set, and the experiment results show that the proposed algorithm can fast generate a set of region proposal with higher localization.