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oai:riuma.uma.es:10630/367592026-02-03T11:01:56Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Salas Espinales, Andrés author Vélez-Chávez, Elián author Vázquez-Martín, Ricardo author García-Cerezo, Alfonso José author Mandow, Anthony author 2024-04-27 Semantic segmentation has been successfully adopted for scenarios such as indoor, outdoor, urban scenes, and synthetic scenes, but applications with scarce labeled data such as search-and-rescue (SAR), have not been addressed. In this work, we propose a transfer learning approach where the U-Net convolutional neural network incorporates ResNet-50 as an encoder for the segmentation of objects in SAR situations. First, the proposed model is trained and validated with 19 classes of the CityScapes dataset. Then we test the proposed approach by i) training the model with a set of 14 Cityscapes classes with relevant similarities to SAR classes, and ii) using transfer learning with the self-developed dataset in SAR scenarios, which has 349 semantic labeled SAR images. The results indicate good recognition in classes with significant presence on the training images. Salas-Espinales A., Vélez-Chávez E., Vázquez-Martín R., García-Cerezo A., Mandow A. U-Net/ResNet-50 Network with Transfer Learning for Semantic Segmentation in Search and Rescue (2024) Lecture Notes in Networks and Systems, 978 LNNS, pp. 244 - 255 DOI: https://www.doi.org/10.1007/978-3-031-59167-9_21 https://hdl.handle.net/10630/36759 10.1007/978-3-031-59167-9_21 Aprendizaje automático (Inteligencia artificial) Rescate U-Net/ResNet-50 Network with Transfer Learning for Semantic Segmentation in Search and Rescue.