Research On Obstacle Detection And Obstacle Avoidance Treatment During Drone Driving

With the continuous advancement of science and technology,the use of drones in social life has become more and more extensive,such as environmental monitoring,agricultural plant protection,aerial photography,surveillance and other investigations.The research on obstacle avoidance processing of drones has always been a research hotspot.At present,the main working principle of UAV obstacle avoidance is to detect obstacles around the drone through different types of sensors,and then take corresponding obstacle avoidance measures.Prevent drones from crashing due to collisions.In the trend of artificial intelligence emerging in recent years,with the widespread application of convolutional neural networks and deep learning,the target detection and recognition method based on deep learning has greatly improved the performance compared with the traditional detection methods and has become a major driving force in the field of target detection.This paper mainly studies the target detection method based on deep convolution network,and designs a new obstacle avoidance processing system,which mainly includes the following work contents:The article first explains the principle of obstacle detection.The emerging deep learning technology-convolution neural network is introduced in detail.The characteristics and improvement of the classical target detection method based on convolutional neural network-RCNN series detection method are introduced.Then a brief introduction to the common database used in the target detection method.Secondly,this paper chooses to use another target detection algorithm(YOLO series target detection algorithm).Compared with the RCNN series,the YOLO series has a faster recognition speed and can be applied to UAV obstacle detection.For the obstacles that the UAV may encounter during driving,such as trees,street lamps,etc.,this paper carries out sample collection of relevant data and makes a data set.For the local database,the YOLOV3 algorithm is used to obtain a better recognition effect.Finally,in the research of obstacle avoidance processing,this paper aims to strengthen the learning obstacle avoidance and consume a lot of computing resources.By adding the event-driven mechanism,it is possible to reduce the number of iterations required while optimizing the calculation amount and make the obstacle avoidance simpler and more effective.At the same time,according to the dynamic obstacles that may be encountered,the local obstacle avoidance algorithm based on dynamic window method is proposed.Different safe distances are set in the face of different obstacles,and a more reasonable and smoother obstacle avoidance path is obtained.