Application of Roadside Perception Method Based on Improved DeepSORT in Surface Mine

Abstract

In the driverless transport and operation system of surface mine, the roadside perception system is used to assist driverless vehicles by providing road condition information. The driverless system currently applied in mine trucks realizes roadside perception based on the multi-sensor fusion technology, consisting of cameras, laser radars and millimeter wave radars. However, this system has several drawbacks, such as high system cost, a complicated structure and poor robustness. In this regard, this paper proposes a roadside perception approach based on an improved DeepSORT algorithm. This approach involves using cameras to acquire image data on vehicles and pedestrians in the mine, which are accurately identified by the YOLOv5s algorithm. Then, the improved DeepSORT algorithm tracks the identified objects in real-time, enabling statistical analysis to provide various functions, including vehicle traffic statistics, abnormal parking detection and pedestrian intrusion detection. The proposed approach was tested at the No. 8 intersection of Xiwan Surface Mine of Shaanxi Shenyan Coal Co., Ltd. The results show that using a single sensor approach can effectively achieve the recognition and tracking of vehicles and pedestrians at mine intersection, reduce the complexity of the roadside perception system and save costs compared to the roadside perception technology based on multi-sensor fusion.

Keywords

• driverless system
• roadside perception
• edge computing
• deep learning
• single sensor
• object detection
• object tracking
• surface mine