Dr. Houda Harkat Profile

Dr. Houda Harkat

at ISR - Instituto de Sistemas e Robotica

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Publications (3)

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Proceedings Article | 26 October 2022 Presentation + Paper

Fire image detection based on clustering data mining techniques

H. Harkat, J. Nascimento, A. Bernardino, H. Farhana Thariq Ahmed

Proceedings Volume 12267, 122670D (2022) https://doi.org/10.1117/12.2636268

KEYWORDS: Wavelets, Flame detectors, Feature extraction, Radon transform, Machine learning, Feature selection, Discrete wavelet transforms, Wavelet transforms, Data mining

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The number of forest fires is growing exponentially with globalization negative impacts and industry evolution. The firefighters are unable to attend fire sources in the desired elapse time. Hence a huge number of forests are destroyed yearly. The statics demonstrate horrible prediction in a time interval of less than ten years. Necessary action and evolution plans must be established to save the globe from an invasive destruction due to the disappear of green areas and consequent disequilibrating ecosystem effects. The obvious idea is to take advantage of current evolution in informatic systems and robotic field, to develop a distance controllable device to scan areas classified as high risk in the vulnerable season (hot season). The first step is to design a machine learning accurate approach to detect fire area on pictures acquired by probable drone or intelligent systems, responsible of the scanning task. Through literature, several approaches were developed treating pictures that are more with afront view of the flames. Training a machine learning algorithm with such pictures with huge areas of flames is feasible. Nonetheless, treating aerial images is not a very easy approach. A deep analysis of the chosen feature engineering technique and machine learning model is required. The current paper accesses the performance of wavelet-based feature extraction technique within different traditional clustering techniques and ranking methods. The results were accessed using different metrics, to show the effectiveness of the approach, namely sensitivity specificity, precision, recall, f-measure, and g-mean.

Proceedings Article | 12 September 2021 Presentation + Paper

Fire segmentation using a SqueezeSegv2

H. Harkat, J. Nascimento, A. Bernardino

Proceedings Volume 11862, 118620D (2021) https://doi.org/10.1117/12.2598566

KEYWORDS: RGB color model, Performance modeling, Content addressable memory, Image segmentation, Data modeling, Convolution, Telecommunications, Remote sensing, LIDAR, Information fusion

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In the last decade, the limitation of the propagation of Wildfire had become a higher necessity. In fact, it is important to optimize the resources used for dislocation to verify the probabilistic signaled fire zones. Hence, using sophisticated and low-cost techniques to sense the previously mentioned zones is highly demanded. Models with high computational necessity are not interesting for real time application. More simple models are requested, to fulfill the desired tasks with an admitted response time. Squeezesegv2 is a model applied initially for LiDAR (Light Detection And Ranging) Point Cloud data segmentation, which gives a high IoU value compared with other state of art architectures. The model was experimented in this paper, it is robust against dropout noise. Experiments were run over RGB pictures of Corsican public French dataset with 1135 RGB images. It is common that highly unbalanced datasets, which is our case, induce high precision low sensitivity. Therefore, several validation measures criterions were adopted to access the performance. In fact, the capability of the model was tested with four different metrics: Accuracy, mean Intersection over Union (IoU), Mean Boundary F1 (BF) Score, and Mean Dice coefficient. The experimental results demonstrate that the trained model, over the Corsican French dataset, with five-fold cross validation procedure can accurately detect the fire flame. The results were collected for different loss function types: Focal loss, Dice and Tversky loss. In general, the given results are very encouraging for further study using deep learning approaches.

Proceedings Article | 20 September 2020 Presentation + Paper

Fire segmentation using a DeepLabv3+ architecture

H. Harkat, J. Nascimento, A. Bernardino

Proceedings Volume 11533, 115330M (2020) https://doi.org/10.1117/12.2573902

KEYWORDS: Network architectures, Flame detectors, Convolution, RGB color model, Climate change, Climatology, Classification systems, Computer programming, Infrared imaging, Infrared radiation

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In the last decade, the number of forest fires events is growing due to the fast change of earth’s climate. Hence, more automatized fire fighting actions had become necessary. Deep learning had drawn interesting results for pixel level classification for smoke detection, but few systems are proposed for fire flame detection. In this paper, a semantic segmentation approach using Deeplabv3+ architecture for wildfire detection is proposed. The network uses Deeplabv3 architecture as encoder and Atrous Spatial Pyramid Pooling (ASPP) which allows to encode multi-scale information and boost the network performance. In fact, the ASPP block concatenates a stack of parallel Atrous convolutions with graduating rates, which produces multi-scale feature map that is further resized. The tests were performed on a public dataset, Corsican fire dataset, which contains 1135 RGB images and 640 infrared pictures. The experiments were conducted on two customized datasets, one using the whole dataset within a single channel information (red and infra-red), and another using only the RGB images set that contains information coded in 3 channels. The used dataset is unbalanced, which could induces high precision with very low sensitivity. Therefore, to measure the performance Dice similarity and Tversky loss functions with cross-entropy are adopted. The capability of the Deeplabv3+ was tested with two different backbones, ResNet18 and ResNet-50, and compared to a very simple Convolutional Neural Network (CNN) architecture with dilated convolution. Four different metrics were used to evaluate the segmentation capability: Accuracy, mean Intersection over Union (IoU), Mean Boundary F1 (BF) Score, and Mean Dice coefficient. The experimental results demonstrate that the Deeplabv3+ with ResNet-50 backbone and a loss function type Dice or Tversky can accurately detect the fire flame, the given results are very encouraging for further study using deep learning approaches.

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