Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images

Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.     

基于注意力机制的实例分割算法

实例分割作为计算机视觉领域极具挑战性的任务之一,要求在图像分类的基础上为每一个物体生成像素级别的分割掩码.业界主流方案可分为自上而下和自下而上两种范式,自上而下范式又可分为双阶段分割和单阶段分割.单阶段分割方案为了提高推断速度,往往使用全图卷积操作取代双阶段分割方案中先检测后分割的策略.然而,卷积网络的平移不变性使得同一种类的不同实例提取到的特征相似,仅靠全图卷积难以进行区分,从而导致单阶段分割方案精度下降.针对单阶段分割精度降低的问题,提出了一种注意力机制,该机制在特征图每个位置的特征向量上进行点积运算,并将运算结果作为新的特征图,同一位置点积结果最大化,不同位置点积结果最小化,以丰富特征图中不同实例的差异信息.通过注意力机制使得单阶段分割方案中的全图卷积操作能更好地区分同一种类的不同实例,从而生成高质量分割掩码.在公开数据集上进行实验,验证了所提方法的有效性.     

基于条纹投射三维测量的铆钉自动化检测技术

针对大型薄壁结构铆接点位自动化检测问题,提出了基于条纹投射三维测量的铆钉检测技术,实现了铆钉镦头尺寸特征高精度测量以及裂纹缺陷自动化识别。在传统条纹投射三维测量的基础上,引入高动态范围(HDR)纹理合成,提出二、三维结合的点云分割策略,实现铆钉尺寸特征高效提取。搭建深度学习神经网络,实现镦头表面缺陷识别。本文搭建了系统原理样机,以铆接桁条样品和标准器作为样件进行系统实验验证。结果表明:实验室条件下,该方法直径测量精度为0.040 mm,高度测量精度为0.013 mm,缺陷识别准确率可达99.30%。与传统方法相比,本文提出的方法更加易于集成,效率高,具有广泛应用价值。