Research on UAV path planning and object detection based on deep learning

At present, unmanned aerial vehicle (UAV) has widely used in tremendous tasks including aerial photography, plant protection, micro-selfies, express transportation, disaster rescue and wildlife observation. However, traditional planning and detection methods is basically precisely impossible in the aerial situation with unstable and complexity inputs. Additionally, existing mechanisms only consider to dispose one issue in a single model that means to utilize a machine to response the path planning and another model to achieve object detection, which may cause numerous system costs and pressure the loads of UAVs. Therefore, we utilize the deep neural network method to train dual neural networks and separately achieve the planning and detection for UAV, which can significantly reduce the system costs and enhance the effectiveness of working. From our comparison results with traditional machine learning algorithms, we can directly observe that our proposed method can increase the detection accuracy and provide multiple reasonable path planning strategies with acceptable system costs.