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Advantages of Unmanned Aerial Vehicle Multi-view Images in Intelligent Matching and Defect Hidden Danger Detection of Substation Inspection
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In order to avoid defects (e.g. dust, scratch, static electricity) caused by manual detection, this research proposes a non-contact defect detection approach for circuit board through use of template matching method. In the proposed approach, images of both circuit board to be detected and circuit board selected as template are obtained respectively from multiple views. Then defect index (DI) of each view is constructed based on corresponding matching results between the image to be detected and the template image. Finally, total defect index is obtained by integrating all DI from multiple views, which can characterize the existence of defects in the circuit board (e.g. lack or wrong installation of components). The proposed approach is experimentally investigated to detect two surface defects on a circuit board. Results show that the two defects can be accurately detected with no false alarm by use the proposed method.
The multi-view total focusing method (TFM) is an imaging algorithm for ultrasonic full matrix array data that exploits internal reflections and mode conversions in the inspected object to create multiple images, the views. Modelling the defect response in multi-view TFM is an essential first step in developing new detection and characterisation methods which exploit the information present in these views. This paper describes a ray-based forward model for small two-dimensional defects and compares its results against finite-element simulations and experimental data for the inspection of a side-drilled hole, a notch and a crack. A simpler version of this model, based on a single-frequency approximation, is derived and compared. A good agreement with the multi-frequency model and a speed-up of several orders of magnitude are achieved.
Wind turbine blade is an important component of wind turbine. Wind turbine blade crack damage will cause hidden danger to the operation of wind turbine. The current wind turbine blade defect detection mainly relies on manual inspection, and the image detection technology can improve the inspection efficiency and reduce the unit maintenance cost. In view of the existing wind turbine blade crack defect detection algorithm with low recognition rate and low accuracy, a YOLOv4-based wind turbine blade crack detection method is proposed. First establish the wind turbine blade crack image dataset, then the anchor box parameters in YOLOV4 are optimized by K-means++ algorithm to make the anchor box parameters match the crack defect size; BiFPN is used instead of PANet to achieve better feature fusion, and finally the Focal Loss function is introduced to balance the number of small size defect samples in the data. The comparison tests show that the AP of the improved YOLOv4 algorithm reaches 93.49, which is better than the original YOLOv4 and the other three comparison algorithms, and has better efficiency and practicability.KeywordsDeep learningObject detectionBlade faultYOLOv4BiFPNFocal lossK-means++
Non-destructive tests are a major evaluation process in the metal, oil, and gas industries. In these industries, weld defect inspection is one of the important parts of testing. Manual inspection may complicate for proper justification and it produces false identification during detection of weld defects in the human perspective less situations. X-radiography testing is utilized in the weld defect inspection and now this method is mostly outdated further it is enhanced into analysis on X-radiography digital image. Therefore, an autonomous weld defect detection and classification is required for the error free inspection. This paper proposed an autonomous technique for weld defects detection and classification using multi-class support vector machine (MSVM) in X-radiography images where weld defects such as porosity, gas pores, tungsten inclusions, longitudinal cracks, lack of penetration, and slag inclusions are considered. Three modules are involved in this proposed method. In the first module, the images were smoothened using modified anisotropic diffusion method. The segmentation process was performed using improved Otsu’s method in the second module. Finally, the features of the region of interest are extracted and given as input to the multi-class support vector machine with the kernel Gaussian radial basis function. The results of proposed scheme were compared with artificial neural network, Bayes and MSVM with other kernel functions like multi-layer perceptron and polynomial. The implementation and experimental outcomes reveal that the proposed scheme can positively detect and classify the weld defects in X-radiography images.
Up to now, the diagnostic imaging was carried out based on manual handling by professional doctors and health care workers. However, it is enable to diagnose images automatically by development of the computer systems. Therefore, it is required for approach from information science and engineering fields. For generating multi-view 3D images, if we are able to support whether workers are able to use correctly or not, and if the coded defect detection and restoration for their images are possible, we consider possibility towards medical applications in the near future. In this paper, first, we diagnosed automatically for multi-view 3D images in the case of occurring defect by encoded and decoded degradation at all or certain viewpoints by H.265/HEVC. Next, we assessed and estimated quantitatively in terms of the coded image quality in order to clarify how we are able to detect the coded defect.
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