Loading Condition Monitoring of Asphalt Mixture in the Mixing Station Based on Semantic Segmentation

Due to poor mobility of the asphalt mixing station, it is necessary to transport the finished asphalt mixture to the designated locations by transportation vehicles. The existing loading methods of transportation vehicles need an operator to observe whether the stack of asphalt mixture in the carriage is full manually and control the switch of the material gate. In order to realize automatic loading of asphalt mixture in the asphalt mixing station, a method of monitoring the loading status of transport vehicles based on semantic segmentation is proposed in this paper. MobileNetV2 is used to be the backbone network of DeepLabV3+ instead of Xception, and a lightweight semantic segmentation network called M-DeepLabV3+ is constructed. The experimental results show that the M-DeepLabV3+ model has an accuracy increase of 0.5% and a speed increase of 16.36%. The recognition method proposed in this paper is almost identical with the manual judgment result, and the accuracy is 98.16%, which lays a foundation for realizing automatic loading of asphalt mixture in the asphalt mixing station.