In order to reproduce clear scenes of visible light images in hazy weather, and effectively suppress the image contrast and clarity degradation caused by haze degradation. General defogging methods do not take into account the uneven distribution of fog concentration and defog the whole image directly. Outdoor scenes of defogging is required to take into account the distribution of fog concentration. In this paper, a natural image defogging method for clarity evaluation indicators is proposed. We select representative indicators for fog concentration classification based on depth information and also obtain global transmittance maps. Compared with traditional method and the deep learning defogging method, The results outperform the other algorithms in different metrics. Experiments show that our results outperform other algorithms in various metrics and are robust to inhomogeneous fog.
To improve the accuracy of outdoor irregular object volume calculation, a signed projection method based on the reference plane is proposed for the determination of the reference plane of the projection method. First, use the Cloth Simulation Filter (CSF) to filter the 3D point cloud model to obtain the ground point cloud; then use the RANSAC algorithm to fit the ground point cloud to obtain the reference plane; secondly, use the signed projection method to calculate the model volume; finally, the true height of the object is calculated by the EPnP algorithm as a scale. In the experimental part, model the iron box and calculate the volume. Comparing the results with the slicing method and the traditional projection method, it is better than other algorithms in terms of accuracy and running time.
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