A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood

Şener, Abdullah; Doğan, Gürkan; Ergen, Burhan

doi:10.1007/s12145-023-01155-9

Earth Sci Inform

2023

DOI: 10.1007/s12145-023-01155-9

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A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood

Abdullah Şener,

Gürkan Doğan,

Burhan Ergen

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2024

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Cited by 3 publications

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A new hybrid mobile CNN approach for crosswalk recognition in autonomous vehicles

Doğan,

Ergen

2024

Multimed Tools Appl

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While automobile transportation is increasing worldwide, it also negatively affects the safety of road users. Along with the neglect of traffic rules, pedestrians account for 22% of all highway traffic deaths. Millions of pedestrians suffer non-fatal injuries from these accidents. Most of these injuries and deaths occur at crosswalks, where the highway and pedestrians intersect. In this study, deep learning-based a new hybrid mobile CNN approaches are proposed to reduce injuries and deaths by automatically recognizing of crosswalks in autonomous vehicles. The first of these proposed approaches is the HMCNet approach, which is a hybrid model in which the MobileNetv3 and MNasNet CNN models are used together. This model achieves approximately 2% more accuracy than the peak performance of the lean used MobileNetv3 and MNasNet models. Another proposed approach is the FHMCNet approach, which increases the success of the HMCNet approach. In the FHMCNet approach, LSVC feature selection method and SVM classification method are used in addition to HMCNet. This approach increased the classification success of HMCNet by more than approximately 2%. Finally, the proposed FHMCNet offered approximately 3% more classification accuracy than state-of-the-art methods in the literature.

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A new hybrid mobile CNN approach for crosswalk recognition in autonomous vehicles

Doğan,

Ergen

2024

Multimed Tools Appl

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A Multilevel Damage Assessment Framework for Mixed-Hazard Buildings With Global Spatial Feature Guidance Module and Change Feature Attention in VHR Remote Sensing Images

Han,

Yang,

Xie

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Unsupervised Color-Based Flood Segmentation in UAV Imagery

Simantiris,

Panagiotakis

2024

Remote Sensing

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We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from unmanned aerial vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood, utilizing calculated masks over each component of the LAB colorspace, as well as using an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying hysteresis thresholding segmentation. The proposed method is tested and compared with variations and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-score, respectively. The results and computational efficiency of the proposed method show that it is suitable for onboard data execution and decision-making during UAV flights.

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A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood

Cited by 3 publications

References 30 publications

A new hybrid mobile CNN approach for crosswalk recognition in autonomous vehicles

A new hybrid mobile CNN approach for crosswalk recognition in autonomous vehicles

A Multilevel Damage Assessment Framework for Mixed-Hazard Buildings With Global Spatial Feature Guidance Module and Change Feature Attention in VHR Remote Sensing Images

Unsupervised Color-Based Flood Segmentation in UAV Imagery

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