Solar panel detection within complex backgrounds using thermal images acquired by UAVs

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds

Intelligent visual otolith classification for bony fish species recognition

The study of otoliths is a well-established source of information for understanding the life offish and fish populations. Conducting fish species identification from otolith samples found in the stomach contents of marine fish-eating animals finds interesting applications such as dietary studies, stock monitoring, assessment and management. Fish species identification can provide useful data for climatology, archaeology and palaeontology research, as otoliths can be sourced from geological sediments or archaeological excavations. Analysing an otolith is a highly complex and time-consuming procedure Therefore, an automated otolith classification system can prove to be a vital tool for a wide variety of scientific research. The aim of the programme of work seeks the development of a novel automated fish species identification system. The main focus of this investigation is on the commercially interesting fish of the Northern Aegean Sea. The methodology described in this thesis exploits the inherent shape variability offish otoliths according to their corresponding species. This is based on the processing and analysis of images acquired using a stereoscopic microscope fitted with a digital camera. A compact feature vector is then constructed out of a list of candidate descriptors derived from the morphology as well as the image statistics of the otoliths. The identification is carried out by an intelligent classifier based on an artificial neural network. Several configurations of multi-layer perceptron, radial basis function and hybrid neural networks are considered in pursuit of a practical and expandable classification system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

GPS-assisted feature matching in aerial images with highly repetitive patterns

Matching aerial images might be challenging when they contain a large number of repetitive patterns. In this paper, we propose a feature-matching method that exploits the use of Affine Oriented FAST and Rotated BRIEF (AORB) as keypoint detector and feature descriptor and not accurate GPS (Global Position System) data to achieve a reliable feature matching of nadir UAV images that contain a large number of repetitive patterns. The proposed method assumes that the set of correct matches between two images only differ in a 2D translation. Experimental results show that the proposed method is able to correctly match pairs of very challenging images containing a large number of repetitive patterns

Expression of endoplasmic reticulum stress markers in the islets of patients with type 1 diabetes.

Endoplasmic reticulum (ER) stress may play a role in cytokine-mediated beta cell death in type 1 diabetes, but it remains controversial whether ER stress markers are present in islets from type 1 diabetic individuals. Therefore, we evaluated by immunostaining the expression of markers of the three main branches of the ER stress response in islets from 13 individuals with and 15 controls without type 1 diabetes (eight adults and seven children).Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe