The Application of Hough Transform and Canny Edge Detector Methods for the Visual Detection of Cumuliform Clouds

The increase in flying time of unmanned aerial vehicles (UAV) is a relevant and difficult task for UAV designers. It is especially important in such tasks as monitoring, mapping, or signal retranslation. While the majority of research is concentrated on increasing the battery capacity, it is also important to utilize natural renewable energy sources, such as solar energy, thermals, etc. This article proposed a method for the automatic recognition of cumuliform clouds. Practical application of this method allows diverting of an unmanned aerial vehicle towards the identified cumuliform cloud and improving its probability of flying into a thermal flow, thus increasing the flight time of the UAV, as is performed by glider and paraglider pilots. The proposed method is based on the application of Hough transform and Canny edge detector methods, which have not been used for such a task before. For testing the proposed method a dataset of different clouds was generated and marked by experts. The achieved average accuracy of 87% on the unbalanced dataset demonstrates the practical applicability of the proposed method for detecting thermals related to cumuliform clouds. The article also provides the concept of VilniusTech developed UAV, implementing the proposed method.

Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) with Harmony Search Optimization (HSO) for Early Breast Cancer Detection System

For women, most common cause of death is Breast tumour and in worldwide, it is the second leading reason for cancer deaths. Due the requirement of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. There are four stages in this proposed HIRResCNN framework, namely, Pre-processing, reduction of dimensionality, segmentation and classification. From images, noises are removed using two filtering algorithms called Median and mean filtering in pre-processing stage. Then canny edge detector is used for detecting edges. Gaussian filtering is used in canny edge detector to smoothen the images. In the next dimensionality reduction stage, attributes are correlated using Principal Component Analysis (PCA) inclusive of related features. So, this huge dataset is minimized and only few variables are used for expressing it. In order to detect the breast cancer accurately, foreground and background subtraction is done in the third stage called segmentation stage. At last, for detecting and classifying breast cancer, a Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) is introduced, which integrates Harmony Search Optimization (HSO) to tune bias and weight parameters and classification accuracy is enhanced using HIRResCNN-HSO model. Strength of Recurrent Convolutional Neural Network (RCNN), Residual Network (ResNet) and Inception Network (Inception-v4), are combined in a powerful Deep Convolutional Neural Network (DCNN) model called HIRResCNN. using Mammographic Image Analysis Society (MIAS) dataset, various experiments are conduced and results are compared with other available techniques. Around 92.6% accuracy rate is produced using this proposed HIRResCNN classifier in finding breast cancer.