Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection
RGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8159 |
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| author | Jianxun Zhao Xin Wen Yu He Xiaowei Yang Kechen Song |
| author_facet | Jianxun Zhao Xin Wen Yu He Xiaowei Yang Kechen Song |
| author_sort | Jianxun Zhao |
| collection | DOAJ |
| description | RGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during information interaction among different scale features, limiting detection performance. To address this issue, we propose a method for RGB-T salient object detection that enhances performance through wavelet transform and channel-wise attention fusion. Through feature differentiation, we effectively extract spatial characteristics of the target, enhancing the detection capability for global context and fine-grained details. First, input features are passed through the channel-wise criss-cross module (CCM) for cross-modal information fusion, adaptively adjusting the importance of features to generate rich fusion information. Subsequently, the multi-scale fusion information is input into the feature selection wavelet transforme module (FSW), which selects beneficial low-frequency and high-frequency features to improve feature aggregation performance and achieves higher segmentation accuracy through long-distance connections. Extensive experiments demonstrate that our method outperforms 22 state-of-the-art methods. |
| format | Article |
| id | doaj-art-2c5f368e796b4345b0f7beb4055fd898 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2c5f368e796b4345b0f7beb4055fd8982025-08-20T02:57:04ZengMDPI AGSensors1424-82202024-12-012424815910.3390/s24248159Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object DetectionJianxun Zhao0Xin Wen1Yu He2Xiaowei Yang3Kechen Song4School of Software Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Software Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Software Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Software Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, ChinaRGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during information interaction among different scale features, limiting detection performance. To address this issue, we propose a method for RGB-T salient object detection that enhances performance through wavelet transform and channel-wise attention fusion. Through feature differentiation, we effectively extract spatial characteristics of the target, enhancing the detection capability for global context and fine-grained details. First, input features are passed through the channel-wise criss-cross module (CCM) for cross-modal information fusion, adaptively adjusting the importance of features to generate rich fusion information. Subsequently, the multi-scale fusion information is input into the feature selection wavelet transforme module (FSW), which selects beneficial low-frequency and high-frequency features to improve feature aggregation performance and achieves higher segmentation accuracy through long-distance connections. Extensive experiments demonstrate that our method outperforms 22 state-of-the-art methods.https://www.mdpi.com/1424-8220/24/24/8159RGB-Tsalient object detectioncross-modal fusionconvolutional neural networks |
| spellingShingle | Jianxun Zhao Xin Wen Yu He Xiaowei Yang Kechen Song Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection Sensors RGB-T salient object detection cross-modal fusion convolutional neural networks |
| title | Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection |
| title_full | Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection |
| title_fullStr | Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection |
| title_full_unstemmed | Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection |
| title_short | Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection |
| title_sort | wavelet driven multi band feature fusion for rgb t salient object detection |
| topic | RGB-T salient object detection cross-modal fusion convolutional neural networks |
| url | https://www.mdpi.com/1424-8220/24/24/8159 |
| work_keys_str_mv | AT jianxunzhao waveletdrivenmultibandfeaturefusionforrgbtsalientobjectdetection AT xinwen waveletdrivenmultibandfeaturefusionforrgbtsalientobjectdetection AT yuhe waveletdrivenmultibandfeaturefusionforrgbtsalientobjectdetection AT xiaoweiyang waveletdrivenmultibandfeaturefusionforrgbtsalientobjectdetection AT kechensong waveletdrivenmultibandfeaturefusionforrgbtsalientobjectdetection |