DASOD: Detail-aware salient object detection

Abstract

Salient object detection (SOD) is a challenging task in computer vision. Current SOD approaches have made significant progress, but they fail in challenging scenarios. This paper categorizes the existing challenges in SOD into four groups: images with complex backgrounds, low contrast, transparent objects, and occluded objects. Then, the Detail-Aware Salient Object Detection (DASOD) method is proposed to address these challenging scenarios. To the best of our knowledge, DASOD is the first method that considers mentioned challenging situations together and detects salient objects in images through camouflaged object detection (COD). DASOD has two main stages: 1) pseudo-mask generation and 2) refinement. It first generates a pseudo-mask using the body label and super-resolution technique, then refines the pseudo-mask with the detail map produced by the pseudo-edge generator to detect salient objects with clear boundaries in the pseudo-mask refinement module. This module quantifies uncertainty using the conditional normalizing flows (cFlow) based conditional variational auto-encoder (cVAE) to generate reliable results. Extensive experiments are conducted on six datasets, and the performance of DASOD is compared with 18 state-of-the-art methods. The results demonstrate that DASOD outperforms its competitors and can accurately detect the salient objects when the image background is cluttered and the contrast between foreground and background is low. Also, it effectively detects the transparent and occluded objects in images. It achieves MAE rates of 0.052, 0.033, 0.027, 0.024, 0.059, and 0.088 on DUT-OMRON, DUTS-TE, ECSSD, HKU-IS, PASCAL-S, and SCAS datasets, respectively. All the implementation source codes and results are available at: https://github.com/BaharehAsheghi/DASOD.