The Tracking System For Ground Moving Objects Based On Deep Learning Framework

In recent years,deep learning and neural networks have been very expressive in image processing.More and more people are investing in deep learning research and trying to solve practical problems in many aspects.The National College Student RoboMasters Competition is a popular robot confrontation competition that has emerged in recent years.Both sides of the game use a variety of ground and air robots to attack each other,requiring both robots to independently identify,track and accurately attack the other robot.Based on the RoboMasters robot competition,this thesis focuses on the theory and application of visual image recognition and tracking technology based on deep learning framework,and completes the construction,debugging and testing of RoboMasters robot's autonomous target recognition and tracking system.This topic mainly studies four aspects: data set establishment,data set processing,network establishment and testing,target recognition and tracking system construction under the deep learning framework,including the following work contents:First,build a data set for a clear tracking target.Aiming at the shortcomings of the traditional labelImg dataset establishment method,a set of automatic labeling algorithm is designed,which can realize the rapid labeling of the dataset.Compared with the labelImg dataset establishment method,the labeling speed is greatly improved.Second,strengthen the built data set.It has studied a variety of processing methods such as image cropping,mirroring,random brightness,random contrast and random saturation.The capacity of the database in the data set has increased by dozens of times,effectively avoiding the phenomenon of neural network overfitting.Then,the target classification and detection methods based on the Single Shot Multibox Detector(SSD)deep learning network are studied.Based on the establishment of the network model,the Pascal VOC2007 data set was used for model verification.The classification results were analyzed by using the precision,recall,F1 and P-R curves respectively.The position detection effect was analyzed comprehensively using IoU and center offset rate,and the frame rate was used to analyze the operation speed.Then,for the shortcomings of slow SSD network operation,a fast SSD network is designed.The network uses a deep separable convolution to replace the ordinary convolution operation,which greatly improves the speed of the neural network.Finally,the ground target automatic identification and tracking platform is designed.The platform uses JetsonTX2 and STM32 as the processor and controller,respectively.Ubuntu and ROS operating systems are used respectively.Under certain test environment,the target recognition and tracking system based on deep learning framework is established.Tested in real time and accuracy.