Research On The Traffic Scene Understanding Method For Intelligent Driving

As an important part of the emerging industries,intelligent driving has become one of the strategic commanding heights of the new economy and technology in the era of artificial intelligence.In the future,It will be a vital technology to solve traffic congestion and improve the production and traffic efficiency greatly.Intelligent driving is of great significance to promote science and technology,economy,society,life,safety & security and comprehensive national strength.In this thesis,we focus on the research of traffic scene understanding method for intelligent driving.Aiming at traffic sign recognition,pedestrian detection and traffic scene semantic understanding,we investigate their problems in practical application.The thesis mainly composed of the following four parts.(1)In view of the problem that the detection effect of traffic signs is easily affected by light,weather and motion blur,a traffic sign detection method based on color probability model is proposed.In this method,the color probability model is established according to the unique color of traffic signs.The ROI(Region of Interest)of traffic signs is extracted from the color probability graph by the MSER(Maximally Stable Extremal Regions),which is determined by image classification method,thus the area of traffic signs is located accurately.This method is verified on the general GTSDB dataset.Because the number of traffic signs is significantly decreased by the ROI extraction method and thus the search space is limited,the detection performance and efficiency are improved.(2)In view of the diversity of clarity and scale of traffic sign images extracted from real scenes,our traffic sign classification method is proposed.It combines the attention-based Fish Net depth feature with the improved color histogram,makes full use of the distinctive color characteristics of traffic signs and the advantages of Fish Net network in general feature extraction,and classifies traffic signs by self-coding network after the fusion of the extracted features.The proposed method is verified on the GTSRB dataset.Compared with other similar methods,the performance of our proposed method is improved obviously in classification.(3)In view of the scale difference caused by the distance between pedestrians and observation points,and the bounding box of pedestrians is rectangle when the pedestrians are walking upright,we propose a multi-scale pedestrian detection model YV3-PD based on YOLOv3.Firstly,the input image data is normalized by scale,and then the up-sampling of the high-level network is spliced twice with the features of the low-level network.After feature reorganization,pedestrian targets of different scales are detected.The combination of high-level network and low-level network makes the low-level feature receptive field unchanged with stronger feature description ability.Feature reorganization changes the aspect ratio of image grid division,so the accuracy of pedestrian detection is improved.The YV3-PD method is tested on both INRIA Person dataset and Caltech Pedestrian dataset.The experimental results show that the detection performance of our method is higher and the miss detection rate is lower.(4)In view of determining the target category in the existing traffic target detection,a traffic scene semantic understanding method based on image description generation technology is proposed.It is the first time to apply image description generation technology to the field of transportation.We created a traffic scene description dataset based on Lara,and introduced a semantic understanding encoder-decoder model based on network feature combination optimization to generate rich semantic information about traffic targets.It can not only describe the traffic targets,but also determine their orientation,which is beneficial for the driving decisions.We carried out the experiments on Flickr30 k and MSCOCO to compare the performance of the proposed method with other classical ones.The experiments on the self-built traffic scene description dataset qualitatively compared the difference between our method and traditional methods,and demonstrated our merits.