RGB-D salient object detection based on multimodal feature information fusion

Lingbing Meng; MengYa Yuan; Xuehan Shi; Qingqing Liu; Weiwei Duan; Fei Cheng; Lingli Li

doi:10.1117/12.2659990

3 February 2023 RGB-D salient object detection based on multimodal feature information fusion

Lingbing Meng, MengYa Yuan, Xuehan Shi, Qingqing Liu, Weiwei Duan, Fei Cheng, Lingli Li

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251103 (2023) https://doi.org/10.1117/12.2659990
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

The key to RGB-D salient object detection is the effective fusion of the different modal features of RGB and depth maps. This study proposes an RGB-D salient object detection method based on multimodal feature information fusion. First, in the encoding stage, essential features from the depth map were extracted using the spatial and channel attention modules and then merged with RGB feature information to improve the expression ability of salient objects. Second, in the decoding stage, a multimodal and multilevel feature fusion module and a global context-feature guidance module were proposed to optimize the detection effect of the network on the salient objects of missing detection and error detection, which can more accurately decode the spatial structure information of multiple objects and small objects. Compared with 15 other deep learning detection methods, the experimental results on four datasets show that our method overcomes the comparison methods on multiple evaluation metrics.

Citation Download Citation

Lingbing Meng, MengYa Yuan, Xuehan Shi, Qingqing Liu, Weiwei Duan, Fei Cheng, and Lingli Li "RGB-D salient object detection based on multimodal feature information fusion", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251103 (3 February 2023); https://doi.org/10.1117/12.2659990

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