Object Detection For Cooperative Driving With Vehicle To Vehicle Communications And Networks

Object detection is a subfield of computer vision that is currently heavily based on machine learning.For the past decade,the field of machine learning has been dominated by so-called deep neural networks,which to ke advantage of improvements in computing power and data availability.A subtype of a neural network called a convolutional neural network(CNN)is well-suited for image-related tasks.The network is trained to look for different features,such as edges,corners and colour differences,across the image and to combine these into more complex shapes.For object detection,the system has to both estimate the locations of probable objects and to classify these.For this master's thesis,we reviewed the current literature on convolutional object detection and tested the implementability of one of the methods.We found that convolutional object detection is still evolving as a technology,despite outranking other object detection methods.By virtue of free availability of datasets and pretrained networks,it is possible to create a functional implementation of a deep neural network without access to specialist hardware.Pretrained networks can also be used as a starting point for training new networks,decreasing costly training time.For the experimental part,we implemented modified model with MobileNet as baseline neural network and Single Shot MultiBox Detector(SSD)as framework to classify and return on KITTI dataset.The benefit of this model is to integrate lightweight convolutional neural network into single shot multibox detector,which both possess the advantage of fast recognition speed.Furthermore,we use visual odometry algorithms as a container to connect object detection with simulated scene in the vehicular communication.The performance of transmission shows it has some of feasibility.