Smoke detection plays an important role in forest safety warning systems and fire prevention. Complicated changes in the shape, texture, and color of smoke remain a substantial challenge to identify smoke in a given image. In this paper, a new algorithm using the deep belief network (DBN) is designed for smoke detection. Unlike popular deep convolutional networks (e.g., Alex-Net, VGG-Net, Res-Net, Dense-Net, and the denoising convolution neural network (DNCNN), specifically devoted to detecting smoke), our proposed end-to-end network is mainly based on DBN. Indeed, most traditional smoke detection algorithms follow the pattern recognition process which consists basically feature extraction and classification. After extracting the candidate regions, the main idea is to perform both smoke recognition and smoke-no-smoke region classification using static and dynamic smoke characteristics. However, manual smoke detection cannot meet the requirements of a high smoke detection rate and has a long processing time. The convolutional neural network (CNN)-based smoke detection methods are significantly slower due to the maxpooling operation. In addition, the training phase can take a lot of time if the computer is not equipped with a powerful graphics processing unit (GPU). Thus, the contribution of this work is the development of a preprocessing step including a new combination of features—smoke color, smoke motion, and energy—to extract the regions of interest which are inserted within a simple architecture with the deep belief network (DBN). Our proposed method is able to classify and localize reliably the smoke regions providing an interesting computation time and improved performance metrics. First, the Gaussian mixture model (GMM) is employed to capture the frames containing a large amount of motion. After applying RGB rules to smoke pixels and analyzing the energy attitude of smoke regions, extracted features are then used to feed a DBN for classification. Experimental results conducted on the publicly available smoke detection database confirm that the DBN has reached a high detection rate that exceeded an average of 96% when tested on different videos containing smoke-like objects, which make smoke recognition more challenging. The proposed methodology provided high detection ratios and low false alarms, and guaranteed robustness verified by evaluations of accuracy, F1-score, and recall for noisy and non-noisy images with and without noise.

The optimal path search is important in navigation learning with an electrical wheelchair. This paper studies six basic approaches for path planning in a static environment: breath-first search, depth-first search, A*, Moore-Dijkstra, neural approach and genetic algorithms. Firstly, the environment is modeled in a 2D grid and the genetic approach has fixed chromosome length. Then the genetic approach is evaluated within a grid free environment and with variable chromosome lengths. The latter approach offers good solution in precision and in computation time, under certain conditions. Tests are led and the results are discussed through experiments.

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

A frequent cause of lower back pain presenting with leg pain is a herniated lumbar intervertebral disc. A herniation or a herniated lumbar disc is a change of position of disc material (nucleus pulpous or annulus fibrosis). Usually, the lower back pain goes away within days or weeks. Regular treatment techniques for lower back pain include medication, exercises, relaxation methods and surgery. Back pain and back problems regularly occur in the lumbar region. The spinal canal is made up of vertebrae; each one protects the spinal nerves. Intervertebral discs and facet joints connect the vertebrae above and below. Groups of muscles and ligaments hold the vertebrae and the discs together. Muscles support the spine and the body weight, and they allow us to move. Pressure can result in excessive wear and tear of the other structures. For example, a common problem in the lower back is disc herniation. In this case, pressure on an intervertebral disc makes its center, the nucleus pulposus, protrude backwards and push against the spinal nerves, leading to lower back pain. Detection and classification are the two most important tasks in computer aided diagnosing systems. Detection of a herniated lumbar disc from magnetic resonance imaging (MRI) is a very difficult task for radiologist. The extraction of herniated discs has been achieved by different approaches such as active contours, region growing, watershed techniques, thresholding and deep learning. In this study, to detect intervertebral disc from axial MRIs we develop a method using generative adversarial networks (GANs), especially the CycleGAN model, to automatically generate and detect intervertebral disc and to classify the type of the herniated lumbar disc such as foraminal or median. We propose to explore the importance of axial view MRI to determine the herniation type. Accurately, GANs and other generative networks have created several ways to tackle different problems well known and challenging of medical image analysis, such as segmentation, reconstruction, data simulation, medical image de-noising, and classification. Moreover, their efficiency to synthesize images and data at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In our case, having a database that contains several images is a very difficult task. In this paper, we put forward a new approach based on GANs, in order to solve the problem of lumbar intervertebral disc images reduction. This method is based especially on CycleGAN. Consequently, the essential objective of our work is to generate and automatically classify the herniation type as foraminal or median using GANs. Our computer aided diagnosis (CAD) system achieved a 97.2% accuracy on our dataset. This result represents a very high-performance results by providing the state of the art and our work utilizing the GANs technique. Our CAD is very effective and efficient for classifying herniations of lumbar intervertebral discs. Therefore, the contribution of this study appears in: firstly, the use of the CycleGAN model based on convolutional layers to detect and classify the herniation type (median or foraminal) in lumbar intervertebral discs, secondly, the use of axial view MRI in order to classify the type of the herniated intervertebral disc. The main objective of this paper is to help radiologists automatically recognize and classify herniated lumbar discs.

In recent decades, the need for advanced systems with good precision, low cost, and high-time response for wildfires and smoke detection and monitoring has become an absolute necessity. In this paper, we propose a novel, fast, and autonomous approach for denoising and tracking smoke in video sequences captured from a camera in motion. The proposed method is based mainly on two stages: the first one is a reconstruction and denoising path with a novel lightweight convolutional autoencoder architecture. The second stage is a specific scheme designated for smoke tracking, and it consists of the following: first, the foreground frames are extracted with the HSV color model and textural features of smoke; second, possible false detections of smoke regions are eliminated with image processing technique and last smoke contours detection is performed with an adaptive nonlinear level set. The obtained experimental results exposed in this paper show the potential of the proposed approach and prove its efficiency in smoke video denoising and tracking with a minimized number of false negative regions and good detection rates.

In recent decades, earlier fire detection has become a research priority. Since visible and infrared images cannot produce clear and complete information, we propose in this work to combine two images with an appropriate fusion technique to improve the quality of fire detection, segmentation, and localization. The visible image is at first weighted before being used in the fusion process. The value of the optimal weight is estimated from the mean of the visible image with a second-order polynomial model. The parameters of this model are optimized with the least squares method from the curve of optimal weights according to the mean of visible images. Finally, a major voting method based on deep learning models is used. Experiments include an assessment of the framework’s performance not only with respect to its visual appearance but also across a spectrum of predefined evaluation criteria. The experiments show that the proposed model, which includes an optimized weighted image fusion stage before segmentation, has a high Intersection over Union (IoU) score of more than 94%.