![]() ![]() This method provides a reasonable solution for unifying information from different modalities but is sensitive to the alignment of the input data. Most existing multi-modal semantic segmentation methods are based on pixel-level aligned sensors, such as RGB and depth cameras, or multi-modal magnetic resonance imaging (MRI). In order to achieve the segmentation task based on multiple spectral data, we believe that multi-modal machine learning (MMML) is a practical approach. In the NIR image, the impact of the shadow on the road will be effectively suppressed, and the road area remains distinguishable in the dark. Thus, changes in intensity in the NIR image are due to material and illumination changes but not to color variations within the same material. It generally penetrates deeper into an object’s surface and can reveal the underlying material characteristics. Therefore, the bands seen in the NIR are typically extensive, leading to spectra that are more complex to interpret compared with RGB spectra. In NIR spectroscopy, light is absorbed in varying amounts by the object at particular frequencies corresponding to the combinations and overtones of vibrational frequencies of some bonds of the molecules in the object. Near-infrared is based on overtones and combinations of bond vibrations in molecules, a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum. Hence, HSI is widely used in various areas, including precision agriculture, military, surveillance, etc. The hyperspectral images can help distinguish different substances, which is difficult in RGB images. Such a large amount of reflectance information about the underlying material can be helpful in accurate HSI segmentation. It has become a mature technology that can capture detailed information for each pixel. ![]() Hyperspectral imaging is technology based on the continuous subdivision of narrow-band spectrums to simultaneously image the target area. A spectral image with a resolution in the range of 10 − 2 λ is called a hyperspectral image. Such problems may be overcome by introducing hyperspectral imaging (HSI) or near-infrared (NIR) images. However, the segmentation method for visible-light images has limitations because of complex surface features in the wild or insufficient illumination at night. Our future goal is to adapt the algorithm to multi-object segmentation and generalize it to other multi-modal combinations. The method can be directly used on the pictures taken in the field without complex preprocessing. The experimental results on a multi-spectral dataset demonstrate that our MMCAN model has achieved state-of-the-art performance. Furthermore, we propose a triplet gate fusion strategy, which can increase the proportion of RGB in the multiple spectral fusion processes while maintaining the specificity of each modality. This transformer promotes the spread and fusion of information between modalities that cannot be aligned at the pixel level. We first introduce a cross-modality transformer using hyperspectral data to enhance RGB features, then aggregate these representations alternatively via multiple stages. In this paper, we propose a novel network named multi-modal cross-attention network (MMCAN) for multi-modal free-space detection with uncalibrated hyperspectral sensors. However, there exist cases where sensors cannot be well-calibrated. Existing multi-modal segmentation methods assume that all the inputs are well-aligned, and then the problem is converted to fuse feature maps from different modalities. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, thus performing well in road segmentation tasks. Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. ![]()
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