为城市景观中的语义分割扩展城市水生场景

Zongcheng Yue, C. Lo, Ran Wu, Longyu Ma, Chiu-Wing Sham
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摘要

在城市环境中,利用计算机视觉进行语义分割对理解和解释城市图像中的各种元素起着关键作用。广泛用于城市场景语义分割的城市景观数据集主要以建筑物和车辆等城市元素为特征,但缺乏水生元素。认识到这一局限性后,我们的研究引入了一种方法来增强城市景观数据集,将水生类别纳入其中,这对全面了解沿海城市环境至关重要。为此,我们采用了双模型方法,使用两个先进的神经网络。第一个网络在标准城市景观数据集上进行训练,而第二个网络则专注于水生场景。我们巧妙地将海洋模型中的水生特征整合到城市景观图像中。我们仔细地执行了这一整合,以确保城市和水生元素的无缝融合,从而创建了一个丰富的数据集,更准确地反映了沿海城市的实际情况。通过在一组不同的城市图像(包括水景)上比较增强的城市景观模型和原始模型,对我们的方法进行了评估。结果表明,我们的方法有效地保持了原始城市景观数据集对城市元素的高分割精度,同时成功地整合了海洋特征。重要的是,实现这一点无需额外的训练,这在资源效率方面具有显著优势。
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Urban Aquatic Scene Expansion for Semantic Segmentation in Cityscapes
In urban environments, semantic segmentation using computer vision plays a pivotal role in understanding and interpreting the diverse elements within urban imagery. The Cityscapes dataset, widely used for semantic segmentation in urban scenes, predominantly features urban elements like buildings and vehicles but lacks aquatic elements. Recognizing this limitation, our study introduces a method to enhance the Cityscapes dataset by incorporating aquatic classes, crucial for a comprehensive understanding of coastal urban environments. To achieve this, we employ a dual-model approach using two advanced neural networks. The first network is trained on the standard Cityscapes dataset, while the second focuses on aquatic scenes. We adeptly integrate aquatic features from the marine-focused model into the Cityscapes imagery. This integration is carefully executed to ensure a seamless blend of urban and aquatic elements, thereby creating an enriched dataset that reflects the realities of coastal cities more accurately. Our method is evaluated by comparing the enhanced Cityscapes model with the original on a set of diverse urban images, including aquatic views. The results demonstrate that our approach effectively maintains the high segmentation accuracy of the original Cityscapes dataset for urban elements while successfully integrating marine features. Importantly, this is achieved without necessitating additional training, which is a significant advantage in terms of resource efficiency.
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