Research On Pedestrian Detection And Early Warning Method Based On Machine Vision

Pedestrian detection and early warning based on machine vision plays an important role in the safe driving of automobiles,and it is one of the research directions that are urgently needed in the fields of cutting-edge vehicle technology and autonomous driving.The research in this article combines detection and early warning to achieve an efficient pedestrian detection and early warning method.Then,by designing a software that operates well on user pages,the results of comparative studies are analyzed and verified through comparative experiments.The main work content as follows:(1)Firstly describe the background and significance of the subject based on pedestrian detection and early warning research methods;then consult related literature to introduce the current research status of pedestrian detection and early warning technologies at home and abroad.Among them,pedestrian detection is divided into traditional types of detection methods: HOG,DPM and ACF,etc,and in the framework of deep learning detection: R-CNN and YOLO "two big families" series of algorithms are briefly explained and compared and analyzed their detection effect.(2)An efficient pedestrian detection method based on YOLOv3 was studied.This algorithm is an improvement on YOLOv3.First,the image features are extracted through simple clustering to obtain the corresponding feature maps.Then the position of the target is determined by the sampling K-means clustering algorithm combined with the kernel function to achieve better clustering.Multi-scale fusion of small target feature information is used to improve the detection effect of small targets.Then,single target,multiple targets,and small target detection under various conditions such as day and night are verified respectively.Experiments verify that pedestrian detection has higher accuracy.(3)Research on pedestrian fuzzy early warning algorithm.This paper uses a fuzzy early warning algorithm to warn pedestrians ahead of the vehicle,and uses the GUI and Fuzzy toolbox that comes with MATLAB software to comprehensively consider the factors that affect pedestrian safety.A safety distance-like model is established for simulation experiments.Based on the relevant driving experience on the basis of the pedestrian's size and location information as the main judgment basis,to comprehensively warn pedestrians in front of the vehicle whether there will be danger,and finally divide the warning results into three levels: safety,attention,danger.The experimental results prove that the fuzzy early warning algorithm has a small calculation amount,fast early warning speed and obvious early warning effect.It can effectively provide different levels of early warning to pedestrians in front of the vehicle and remind the driver to deal with it.Finally,Py Qt is used to design and develop a pedestrian detection software that is simple to use and has high detection accuracy.According to the software's functions and practical performance requirements,Py Qt is used to design the software interface,and write programs to implement the pedestrian warning function.The experimental results show that the designed software can run accurately and stably.