Fast, non-destructive and on-site quality control tools, mainly high sensitive imaging techniques, are important to assess the reliability of photovoltaic plants. To minimize the risk of further damages and electrical yield losses, electroluminescence (EL) imaging is used to detect local defects in an early stage, which might cause future electric losses. For an automated defect recognition on EL measurements, a robust detection and rectification of modules, as well as an optional segmentation into cells is required. This paper introduces a method to detect solar modules and crossing points between solar cells in EL images. We only require 1-D image statistics for the detection, resulting in an approach that is computationally efficient. In addition, the method is able to detect the modules under perspective distortion and in scenarios, where multiple modules are visible in the image. We compare our method to the state of the art and show that it is superior in presence of perspective distortion while the performance on images, where the module is roughly coplanar to the detector, is similar to the reference method. Finally, we show that we greatly improve in terms of computational time in comparison to the reference method.
The detection of solar modules in an EL image is an object detection task. Traditionally, feature-based methods have been applied to solve the task of object detection. Especially, Haar wavelets have proven to be successful @cite_0 . For an the efficient computation, Viola and Jones @cite_12 made use of integral images, previously known as summed area tables @cite_9 . Integral images are also an essential part of our method.
@cite_0 @cite_9 @cite_12
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The integral image is typically used for fast integrating a function over a rectangular region in an image. We propose a method that extends the integral image to do fast integration over the interior of any polygon that is not necessarily rectilinear. The integration time of the method is fast, independent of the image resolution, and only linear to the polygon's number of vertices. We apply the method to Viola and Jones' object detection framework, in which we propose to improve classical Haar-like features with polygonal Haar-like features. We show that the extended feature set improves object detection's performance. The experiments are conducted in three domains: frontal face detection, fixed-pose hand detection, and rock detection for Mars' surface terrain assessment. We present an improvement to the object detection scheme of Viola and Jones (2004), using the example of face detection. We present a new kind of visual features that is based on sampling individual pixels within the examined sub-window, instead of comparing the sums of pixel values in rectangular regions. Our features are faster and do not demand the preparation of an integral image, nor variance-normalisation of each sub-window. The result is an improved system that detects faces in quarter-PAL images in only 9 milliseconds per image on a 2.4-GHz pentium computer. Robust and rapid object detection is one of the great challenges in the field of computer vision. This paper proposes a hardware architecture suitable for object detection by Viola and Jones based on an AdaBoost learning algorithm with Haar-like features as weak classifiers. Our architecture realizes rapid and robust detection with two major features: hybrid parallel execution and an image scaling method. The first exploits the cascade structure of classifiers, in which classifiers located near the beginning of the cascade are used more frequently than subsequent classifiers. We assign more resources to the former classifiers to execute in parallel than subsequent classifiers. This dramatically improves the total processing speed without a great increase in circuit area. The second feature is a method of scaling input images instead of scaling classifiers. This increases the efficiency of hardware implementation while retaining a high detection rate. In addition we implement the proposed architecture on a Virtex-5 FPGA to show that it achieves real-time object detection at 30 frames per second on VGA video.