Semantic segmentation of roads in remote-sensing images is a challenging task. This paper proposes a semantic segmentation model, DANet, for remote-sensing image road semantic segmentation. The model addresses the problems of missing, misclassification, and strong jaggedness of segmented target edges faced by other semantic segmentation networks when dealing with complex and diverse remote-sensing images. The proposed model uses two ASPP structures for multi-scale feature fusion and combines the DarkNet network structure for downsampling with the SegNet network structure for upsampling. This improves the model’s ability to extract road feature information from remote-sensing images. Using the CHN–CUG Roads Dataset, we have confirmed that the proposed network structure, Re, has demonstrated a 1.15% improvement in accuracy compared to U-Net. Furthermore, the road IoU has shown a 1.09% enhancement compared to HRNet-V2. Additionally, there is a 1.13% increase in F1-score compared to U-Net.

Urban street lighting system plays an important role in urban traffic lighting and road safety. With it, the power consumption of urban street lights is increasing, and the cost is also increasing year by year. The purpose of this paper is to detect and classify targets through road monitoring radar, so as to achieve the purpose of real-time detection of pedestrians, vehicles and other targets, so as to control the brightness of street lights to save power consumption. This paper firstly establishes the radar echo model of various targets, deduces the model formula of radar echo, and analyzes the micro-Doppler characteristics produced by the micro-moving parts of the target. Secondly, Secondly, the ResNet residual neural network training method is used to analyze the target micro-Doppler information, extract the depth information, and realize the target detection and recognition task.

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