Semantic segmentation of mix loading at asphalt mixing plant based on improved U-Net

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.