Research on image saliency detection based on deep neural network

Abstract As a hot research field at present, computer vision is devoted to the rapid acquisition and application of target information from images or videos by simulating human visual mechanism. In order to improve the accuracy and efficiency of image detection, image saliency region detection technology has received more and more attention in the field of computer vision research; an important research content in the field, the core part of which lies in the research on algorithms related to feature extraction and saliency calculation of targets. This paper analyzes the multi‐feature fusion saliency detection model and visual saliency calculation process, and based on the existing algorithm, by improving the VGG16 network, a fully convolutional network saliency detection algorithm is proposed. The qualitative and quantitative experimental results show that compared with the four mainstream methods of BL, GS, SF, and RFCN, our algorithm not only improves the accuracy of salient object detection, but also effectively solves the problem of target edge blur. Therefore, this study has improved the accuracy and efficiency of saliency detection, which can not only promote the development of computer vision technology, but also provide support for research in the field of image processing.