Zhao Jianxun, Wen Xin, He Yu, Yang Xiaowei, Song Kechen
School of Software Engineering, Shenyang University of Technology, Shenyang 110870, China.
School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2024 Dec 20;24(24):8159. doi: 10.3390/s24248159.
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.
RGB-T显著目标检测（SOD）在计算机视觉领域受到了广泛关注。尽管现有方法在某些场景中取得了显著的检测性能，但挑战依然存在。许多方法在不同尺度特征之间的信息交互过程中未能充分利用高频和低频特征，从而限制了检测性能。为了解决这个问题，我们提出了一种用于RGB-T显著目标检测的方法，该方法通过小波变换和通道注意力融合来提高性能。通过特征区分，我们有效地提取了目标的空间特征，增强了对全局上下文和细粒度细节的检测能力。首先，将输入特征通过通道交叉模块（CCM）进行跨模态信息融合，自适应地调整特征的重要性以生成丰富的融合信息。随后，将多尺度融合信息输入到特征选择小波变换模块（FSW）中，该模块选择有益的低频和高频特征以提高特征聚合性能，并通过长距离连接实现更高的分割精度。大量实验表明，我们的方法优于22种先进方法。
