Guidance and Salient Object Detection of Experimental Operations Based on Fourier Transform

The accelerated development of deep learning has facilitated the widespread application of policing technologies in practical scenarios. Salient object detection is a research topic in the field of computer vision that has attracted great interest for a variety of tasks. It constitutes a fundamental component of a wide range of vision-related tasks. The Fourier transform is a valuable way of interchanging information between time and frequency domains, which is conducive to understanding abstract semantic information. This paper proposes a novel salient object detection framework based on the Fourier transform to capture richer target details and contextual information, thereby improving the saliency prediction. The experimental results demonstrate that our proposed method exhibits superior performance compared to existing state-of-the-art methods on five benchmark databases. Moreover, the results demonstrate the potential of our proposed approach for addressing experimental manipulation issues in a practical setting.