Saliency detection has become a highly active research field in recent years. Considering that many traditional methods suffer from insufficient feature learning and bad robust detection

this study proposes a novel saliency detection model based on deep convolutional neural networks. First

a pixel with similar characteristics is clustered by using superpixels and the target edge is extracted by imitating the human visual cortex cell to obtain the region and edge features. Thereafter

image regions and edge features are identified by convolutional neural networks to obtain the corresponding target-detection decision confidence images. Finally

we introduce the output of the deep-convolution neural network confidence coefficient into the conditional random field to calculate energy minimization. The discrimination of saliency and non-saliency is realized to complete the saliency detection task. Compared with the state-of-the-art method

the detection accuracy of our algorithm increases by approximately 1.5% in the MSAR database and 5% in the Berkeley database. Furthermore

our detection algorithm produces the best results whether in natural/artificial construction scenarios or large/small objects. Our detection algorithm can avoid the uncertainty brought by manual features and has high robustness and universality. Experimental results show the superiority of our proposed algorithm to the method using shallow artificial features. Both subjective visual pleasure and objective detection accuracy attest the effectiveness of the proposed algorithm.