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Aiming at the problems of inaccurate semantic segmentation results and rough saliency maps of indoor scene images, this paper proposed a network architecture (feature regulator and dual-path guidance, FG-Net) based on multi-modal feature optimization extraction and dual-path guided decoding. Specifically, the feature regulator sequentially performed noise filtering, re-weighted representation, differential complementation and interactive fusion on the multi-modal features at each stage, and optimized multi-modal feature representation in the feature extraction process by strengthening RGB and depth feature aggregation. Then, the dual-path guidance component introduced rich cross-modal cues after feature interactive fusion in the decoding stage to further take advantage of multi-modal features. The dual-path cooperative guidance structure outputted a more detailed saliency map by integrating multi-scale and multi-level feature information in the decoding stage. This paper conducted experiments on the public datasets NYUD-v2 and SUN RGB-D, and achieved 48.5% in the main evaluation metric mIoU, which is better than other state-of-the-art algorithms. The results show that the algorithm achieves more refined semantic segmentation of indoor scene images, and has good generalization and robustness. [ABSTRACT FROM AUTHOR]

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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)