Hardware-Efficient Two-Stage Saliency Detection

Saliency detection, or salient object detection, is an essential pre-processing step for many computer vision applications. It extracts the most conspicuous part of an image and reduces the computation and transmission requirement. This ability is desired for end devices with limited hardware resources. However, existing algorithms are not suitable for hardware implementation. Traditional works usually build upon manually designed priors, and their computations usually involve irregular memory access. Recently, deep learning based algorithms have demonstrated superior performance, while they require a large number of parameters and computation. In this paper, we propose a hardware-efficient algorithm for salient object detection. Our algorithm first uses a lightweight CNN to predict a coarse saliency map, which is then refined to obtain the boundary-accurate saliency map. We demonstrate that our two stage algorithm can achieve favorable performance compared to existing methods while being more hardware-efficient regarding computation and memory requirement.