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引用次数: 1
摘要
城市街道场景理解是自动驾驶应用感知任务的重要组成部分。语义分割在场景理解中得到了广泛的应用,进一步为后续的自动驾驶任务,如物体检测、路径规划和运动控制提供了帮助。但是,在计算机视觉中,准确的语义分割是一项具有挑战性的任务。U-Net是一种流行的语义分词网络,用于分词任务。本文采用卷积神经网络(CNN)结构代替U-Net模型的编码器部分,提高了U-Net模型的精度。我们比较了VGG-16和ResNet-50 cnn架构的性能。在cityscape数据集上进行了广泛的分析,结果表明使用VGG16编码器的U-Net比使用ResNet50编码器的U-Net具有更好的性能。该模型与全卷积网络(Fully Convolutional Network, FCN)和SegNet等语义分割CNN架构进行了比较,平均交联数(Intersection over Union, mIoU)提高了2%。
Semantic Segmentation using Modified U-Net for Autonomous Driving
Scene understanding of urban streets is a crucial component in perception task of autonomous driving application. Semantic segmentation has been extensively used in scene understanding which further provides assistance in subsequent autonomous driving tasks like object detection, path planning and motion control. But, accurate semantic segmentation is a challenging task in computer vision. U-Net is a popular semantic segmentation network used for segmentation task. In this paper, we improve the accuracy of U-Net model by replacing its encoder part with Convolution Neural Network (CNN) architecture. We compared the performance of VGG-16 and ResNet-50 CNNarchitectures. Extensive analysis was performed on Cityscapes dataset and the results demonstrated U-Net with VGG16 encoder shows better performance than ResNet50 encoder. The model is compared with semantic segmentation CNN architectures like Fully Convolutional Network (FCN) and SegNet with mean Intersection over Union (mIoU) improved by 2%.