Image salient target detection method combined with deep learning

Abstract

The invention provides an image salient target detection method combined with deep learning. The method is based on an improved RFCN deep convolution neural network of cross-level feature fusion, anda network model comprises two parts of basic feature extraction and cross-level feature fusion. The method comprises: firstly, using an improved deep convolution network model to extract features of an input image, and using a cross-level fusion framework for feature fusion, to generate a high-level semantic feature preliminary saliency map; then, fusing the preliminary saliency map with image bottom-layer features to perform saliency propagation and obtain structure information; finally, using a conditional random field (CRF) to optimize a saliency propagation result to obtain a final saliency map. In a PR curve graph obtain by the method, F value and MAE effect are better than those obtained by other nine algorithms. The method can improve integrity of salient target detection, and has characteristics of less background noise and high algorithm robustness.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image detection method based on a deep learning computer mathematical model. Background technique [0002] In today's era of Internet popularization and explosion of image information, it is becoming more and more difficult for human beings to process external image information only through their own visual perception system, and using computers to process image information has become an effective method. The researchers simulated the visual perception mechanism that humans can quickly pay attention to the region of interest in the image, and proposed an image salient object detection method. Due to the ability to extract the key information of the image scene, the amount of calculation is greatly reduced when performing subsequent processing under limited resources. [1] , image scene understanding [2] , target perception [3] , image and video compression [4] and ...

AI Technical Summary

Problems solved by technology

Compared with traditional methods, learning-based methods have further breakthroughs in performance, but there are also problems such as lack of prominent high-level semantic features, feature redundancy, lack of structural information, and more discrete noise.

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