Repairing Real-Time Requirements

Noguchi, Reiya; Sankur, Ocan; Jéron, Thierry; Markey, Nicolas; Mentré, David

doi:10.1007/978-3-031-19992-9_24

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…Retraining the Cityscapes dataset to include aquatic environments poses significant challenges, including high computational demands and extensive time requirements [12,13]. Simplification techniques, such as fixed-point calculations [14] or binary neural networks [15][16][17], offer some relief, but their impact is marginal given the scale and complexity of the data [18].…”

Section: Introductionmentioning

confidence: 99%

Urban Aquatic Scene Expansion for Semantic Segmentation in Cityscapes

Yue,

Lo,

et al. 2024

Urban Science

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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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