Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Enhancement of digital chest images using a modified Sobel edge detection algorithm

Computed tomography (CT) images are an essential factor in the diagnosing procedure for various diseases affecting the internal organs. Edge detection can be used for the appropriate enhancement of the lung CT scan images for the diagnosis of the various interstitial lung diseases (ILD). In order to solve the issues of edge detection provided by the traditional Sobel operator, the paper proposes a Sobel 12D edge detection algorithm which uses the additional direction templates for the better identification of the edge details. First, the vertical and horizontal directions available in the traditional Sobel operator are extended to few more directions (a total of 12 directions) which enhances the edge extraction ability. Next part, compute the edge detected image using the Sobel 12D, Laplace, Prewitt, Robert’s Cross and Scharr operators for edge detection separately. It is followed by image fusion method which optimizes the edge detection by combining the edge detected images obtained using the Sobel 12D approach and the Laplace operator. The experimental results shows that the proposed algorithms generates a better detection of the edges than the other edge detection operators.

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography ( CT ) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data ( SCLLD ) relying on Sobel edge detection and Generative Adversarial Networks ( GANs ) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network ( CNN ) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 ± 0.20%, 99.88 ± 0.24%, and 99.40 ± 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 ± 4.11%, 91.2 ± 6.15%, and 46.40 ± 5.21%.

An optimal segmentation method for processing medical image to detect the brain tumor

In the field of medical physics, detection of brain tumor from computed tomography (CT) or magnetic resonance (MRI) scans is a difficult task due to complexity of the brain hence it is one of the top priority goals of many recent researches. In this article, we describe a new method that combines four different steps including smoothing, Sobel edge detection, connected component, and finally region growing algorithms for locating and extracting the various lesions in the brain. The computational algorithm of the proposed method was implemented using Insight Toolkit (ITK). The analysis results indicate that the proposed method automatically and efficiently detected the tumor region from the CT or MRI image of the brain. It is very clear for physicians to separate the abnormal from the normal surrounding tissue to get a real identification of related areas; improving quality and accuracy of diagnosis, which would help to increase success possibility by early detection of tumor as well as reducing surgical planning time. This is an important step in correctly calculating the dose in radiation therapy later.

Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection

China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.

A New Image Processing Technique for Medical Image Analysis

In this communication, we report identification of lung cancer with the help of a new image processing technique. To start with, we compare the results obtained from our proposed method with those obtained from the original Sobel edge detection method. The comparison is done by creating a confusion matrix and considering various parameters such as True Positive, True Negative, False Positive, False Negative, Accuracy, Misclassification Rate, Precision, Recall, Specificity, F Score, Matthews Correlation Coefficient (MCC), F Measurement (FM), Z score, T Score, and P Score. Our proposed method gives better total accuracy than the normal Sobel edge detector. Keywords: Neural network, statistical learning, edge filters, deep learning, image processing

Innovative approach to evaluate the wearing of nano TiO2 doped alumina ceramics in the light of image modelling

The present research is emphasized on the microscopic observation of post wear surface of nano TiO2 doped alumina ceramics to accesses wearing by promising image processing algorithms viz. entropy analysis, Sobel edge detection technique and entropy filtered image histogram analysis in relation to the extent of doping. The experimental results of specific wear rate showed an indicator with the extent of micro fracturing of grains, ploughing of materials and debris formation on the wear track after a long wear cycles in terms of entropy level, edge density index, entropy filtered image and the nature of histogram at different doping level. The lowest value of entropy level and edge density index is shown at the level of 1 wt.% TiO2 doped alumina ceramics due to the presence of low number of granularity and microfracture grains on the wear track causes the lowering of specific wear rate. The histogram of entropy filtered image for 1 wt.% doping is more uniformly distributed with the highest frequency and lowest skewness factor over a wide range of intensity values for 1 wt.% doping.