DESIGN OF 3D POINT CLOUD BLIND GUIDE SYSTEM BASED ON IMPROVED YOLOV4

Aimed at the problem that traditional blind guide system is susceptible to environmental interference and has low recognition accuracy, a 3D point cloud blind guide system based on YOLOv4 is proposed. This design used the high precision laser scanner to collect point cloud road information which was sent to the cloud server and converted into projection image containing feature information. The mode established road condition data set and built a network model based on DARKNET training framework to identify the complex road conditions. K-means+[KG-*3]+ algorithm was used to improve the original clustering algorithm of YOLOv4 model and improve the adaptability of multi-scale detection of the model. The experimental results show that the average accuracy of the system for identifying complex road conditions is 98.12%. Compared with similar products, it can accurately and stably identify road conditions and obstacles.