Quan Zhenhua, Wu Bin, Luo Liang
Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621900, China.
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China.
Sensors (Basel). 2023 Sep 29;23(19):8179. doi: 10.3390/s23198179.
With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot's ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network's representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions.
随着人工智能技术和计算机硬件的发展，立体匹配算法在图像处理领域得到了广泛的研究和应用。在机器人导航和自动驾驶等场景中，立体匹配算法用于帮助机器人获取周围环境的深度信息，从而提高机器人在自动驾驶过程中的自主导航能力。在本文中，我们解决了立体匹配算法在图像镜面区域匹配精度较低的问题，并提出了一种基于多注意力的立体匹配算法MANet。该算法将多光谱注意力模块嵌入到PSMNet算法的残差特征提取网络中。它利用不同的二维离散余弦变换来提取特定频率的特征信息，为匹配中的代价计算提供丰富而有效的特征。金字塔池化模块采用了协同注意力机制，不仅能保持具有方向感知的长距离依赖性，还能在池化过程中捕获更多的位置信息，从而增强网络的表征能力。MANet算法在SceneFlow、KITTI2015和KITTI2012这三个主要基准数据集上进行了评估，并与相关算法进行了比较。实验结果表明，MANet算法在预测视差方面具有更高的精度，并且对镜面反射表现出更强的鲁棒性，能够在镜面区域进行更准确的视差预测。
