中圖分類號：U415 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.245955 中文引用格式： 李東麗，成高立，郭濤，等. 基于改進U-Net的瀝青拌合站混合料裝車語義分割[J]. 電子技術應用，2025，51(4)：29-34. 英文引用格式： Li Dongli，Cheng Gaoli，Guo Tao，et al. Semantic segmentation of mix loading at asphalt mixing plant based on improved U-Net[J]. Application of Electronic Technique，2025，51(4)：29-34.
Semantic segmentation of mix loading at asphalt mixing plant based on improved U-Net
Li Dongli1，Cheng Gaoli1，Guo Tao2，Xia Xiaohua2
1.Shaanxi Expressway Mechanization Engineering Limited Company； 2.Key Laboratory of Road Construction Technology and Equipment of MOE， Chang'an University
Abstract： Aiming at the existing asphalt mixing plant mixture loading semantic segmentation methods with low Mean Intersection over Union(mIoU) values and slow detection speed, a lightweight network RCS-UNet is proposed for semantic segmentation of asphalt mixing plant mixture loading state.Firstly, residual connections are integrated into the U-Net network to mitigate the gradient vanishing issue, promoting stability during training, enhancing convergence speed, and improving generalization abilities. Secondly, the Coordinate Attention(CA) mechanism is incorporated to boost the perception of positional and channel information, refining feature extraction and enabling a sharper focus on critical regions within the image. Finally, the standard convolution in the U-Net network is modified to depth-separable convolution in order to reduce the size and parameters of the model, so that the model has a lower resource consumption and a faster inference speed while maintaining a higher performance. The experimental results show that the accuracy, mIoU, and FPS of the improved model are 99.20%, 98.41% and 22.98, respectively, which are the highest compared with the classical model and the current state-of-the-art model. The best segmentation results are obtained.
Key words : residual connectivity；CA；depth-separable convolution；semantic segmentation；asphalt mixing plant
