Research On Fully Convolutional Networks For Semantic Segmentation And Its Application In Drivable Area Segmentation

With the high-speed development of artificial intelligence,computer vision technology has become a research hotspot,and deep learning convolutional neural network has been validation is a very effective method in the field of computer vision.Image semantic segmentation is a cornerstone of computer vision technology,image semantic segmentation algorithm directly determines the quality of image recognition and understanding,so the effective implementation and application of the image semantic segmentation algorithm has very important practical significance.This paper mainly refers to the image semantic segmentation by means of deep learning.Using the standard dataset VOC2012,the semantic segmentation experiment was carried out with the Fully Convolutional Networks FCN.Then,based on the in-depth study,the hole convolutional algorithm is introduced to replace the bottom sampling in the last two maxpool layers in the VGG network,and improve the feature resolution and also guarantee the size of the sensory field.Then add the fully connected condition random fields after the output feature to improve the ability of the model to acquire feature details.Finally,the experimental results are compared and analyzed through the training standard dataset VOC2012 and the consolidated dataset of the enhanced dataset.Experiments show that the improved algorithm is better than the original algorithm in segmentation precision and edge optimization.Finally,I apply the FCN algorithm to the drivable area segmentation of the Autonomous vehicles,according to the segmentation experiments using small amount data set,based on the deep VGG network model,with smaller semantic segmentation experiment network model,reduces the weight of the network model,and reduce the over fitting phenomenon.Through training and the original network model experiment of the training time,this paper compares and analyzes the accuracy degree of convergence and loss value curve,verify the adaptation of the network model can achieve the training time is short,high operation efficiency,the purpose of thesegmentation precision.Finally,the segmentation test of small model is carried out,and its advantages and disadvantages are analyzed according to its segmentation results.