Existing saliency object detection (SOD) methods struggle to satisfy fast
inference and accurate results simultaneously in high resolution scenes. They
are limited by the quality of public datasets and efficient network modules for
high-resolution images. To alleviate these issues, we propose to construct a
saliency object matting dataset HRSOM and a lightweight network PSUNet.
Considering efficient inference of mobile depolyment framework, we design a
symmetric pixel shuffle module and a lightweight module TRSU. Compared to 13
SOD methods, the proposed PSUNet has the best objective performance on the
high-resolution benchmark dataset. Evaluation results of objective assessment
are superior compared to U$^2$Net that has 10 times of parameter amount of our
network. On Snapdragon 8 Gen 2 Mobile Platform, inference a single
640$\times$640 image only takes 113ms. And on the subjective assessment,
evaluation results are better than the industry benchmark IOS16 (Lift subject
from background).

为解决现有显著性目标检测（SOD）方法在高分辨率场景中无法同时满足快速推理和准确结果的问题，本文提出了构建高分辨率显著性目标抠图数据集 HRSOM 和轻量级网络 PSUNet 的方法。在单个 640×640 像素图像上运行的推理时间仅为 113 毫秒，在模型客观评估和主观评估方面均优于行业标杆 IOS16。