The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.

Semantic segmentation of urban scenes is essential in urban traffic analysis and road condition information acquisition. The semantic segmentation model with good performance is the key to applying high-resolution urban locations. However, the types of these images are diverse, and the spatial relationships are complex. It is greatly affected by weather and light. Objects of different scales pose significant challenges to image segmentation of urban scenes. The existing semantic segmentation is mostly solved from the target scale and superpixel methods. Our research mainly fills the gap in image segmentation field of ConvNeXt fusion U-Net pyramid network model in specific urban scenes. These methods could be more accurate. Therefore, we propose the multi-scale fusion deformation residual pyramid network model method in this paper. This method captures features of different scales and effectively solves the problem of urban scene image segmentation of memory scenes by objects of different scales. We construct a spatial information interaction module to reduce the semantic ambiguity caused by complex spatial relations. By combining spatial and channel characteristics, a series of problems caused by weather and light can be alleviated. We verify the improved semantic segmentation model on the Cityscape dataset. The experimental results show that the method achieves 84.25% MPA and 75.61% MIoU. Our improved algorithm, ConvNeXt embedding in the U-Net algorithm architecture, is named Conv-UNet. The improved method proposed in this paper is superior to other methods in the semantic segmentation of urban scenes. The main advantage of this algorithm is to explore the specific loss function and segmentation strategy suitable for urban scene in the face of the complexity and diversity of urban scene images.