Deep CNN Based System for Detection and Evaluation of RoIs in Flooded Areas

Popescu, Dan; Ichim, Loretta; Cioroiu, George

doi:10.1007/978-3-030-36708-4_20

Cited by 1 publication

(11 citation statements)

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“…A weight (corresponding to the confidence level) was established in the validation phase for each neural network. Based on the results of the previous works [46] and [39], a fusion system with increasing performance was proposed and implemented for flood and vegetation assessment. The following types of neural networks are considered as primary classifiers (PCs): YOLO, cGAN, LeNet, AlexNet, and ResNet.…”

Section: Methodsmentioning

confidence: 99%

“…The selection of CNN was based on our previous studies [24], [29], and [39] and also on the consultation of other relevant works. We considered individual networks as subjective classifiers based on their structure and learning.…”

Section: Methodsmentioning

confidence: 99%

“…Initially, YOLO was a convolutional network designed to detect and frame objects in an image. Subsequently, it was used to classify these objects and, then, segment the framing areas [37][38][39]. YOLO v3 [40] is extremely fast and treats the detection of the regions of interest as a regression problem by dividing the input image into a grid of size m × m, and for each cell in that grid it determines the probability that it belongs to a class of interest.…”

Section: Using Deep Cnnmentioning

confidence: 99%

“…The results demonstrate that the proposed method provides high accuracy and robustness compared with other methods for flooding evaluation. Other types of deep CNN like LeNet [32] (full LeNet, half LeNet) and YOLO v3 (pixel YOLO, decision YOLO) were used separately in [39] for detection and evaluation of RoIs in flooded zones. These CNNs, partially adapted from the existing literature, used a transfer learning procedure.…”

Section: Using Deep Cnnmentioning

confidence: 99%

“…The CNN named decision YOLO, proposed in [39], operates at a global level, and the architecture is presented in Figure 3. The network has only convolutional layers grouped in two parts: down sampling and up sampling.…”

Section: Yolo Networkmentioning

confidence: 99%

See 4 more Smart Citations

Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks

Ichim

Popescu

2020

Remote Sensing

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The detection and evaluation of flood damage in rural zones are of great importance for farmers, local authorities, and insurance companies. To this end, the paper proposes an efficient system based on five neural networks to assess the degree of flooding and the remaining vegetation. After a previous analysis the following neural networks were selected as primary classifiers: you only look once network (YOLO), generative adversarial network (GAN), AlexNet, LeNet, and residual network (ResNet). Their outputs were connected in a decision fusion scheme, as a new convolutional layer, considering two sets of components: (a) the weights, corresponding to the proven accuracy of the primary neural networks in the validation phase, and (b) the probabilities generated by the neural networks as primary classification results in the operational (testing) phase. Thus, a subjective behavior (individual interpretation of single neural networks) was transformed into a more objective behavior (interpretation based on fusion of information). The images, difficult to be segmented, were obtained from an unmanned aerial vehicle photogrammetry flight after a moderate flood in a rural region of Romania and make up our database. For segmentation and evaluation of the flooded zones and vegetation, the images were first decomposed in patches and, after classification the resulting marked patches were re-composed in segmented images. From the performance analysis point of view, better results were obtained with the proposed system than the neural networks taken separately and with respect to some works from the references.

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Section: Methodsmentioning

confidence: 99%