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This paper presents the development procedure of the feature extraction and classification module of an intelligent sorting system for electronic components. This system was designed as a prototype to recognise six types of electronic components for the needs of the educational electronics laboratories of the Kavala Institute of Technology. A list of features that describe the morphology of the outline of the components was extracted from the images. Two feature selection strategies were examined for the production of a powerful yet concise feature vector. These were correlation analysis and an implementation of support vector machines. Moreover, two types of neural classifiers were considered. The multilayer perceptron trained with the back-propagation algorithm and the radial basis function network trained with the K-means method. The best results were obtained with the combination of SVMs with MLPs, which successfully recognised 92.3% of the cases. [ABSTRACT FROM AUTHOR]

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 F 1 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 F 1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds. [ABSTRACT FROM AUTHOR]