Defect Detection on a Wind Turbine Blade Based on Digital Image Processing

Wind power generation is a widely used power generation technology. Among these, the wind turbine blade is an important part of a wind turbine. If the wind turbine blade is damaged, it will cause serious consequences. The traditional methods of defect detection for wind turbine blades are mainly manual detection and acoustic nondestructive detection, which are unsafe and time-consuming, and have low accuracy. In order to detect the defects on wind turbine blades more safely, conveniently, and accurately, this paper studied a defect detection method for wind turbine blades based on digital image processing. Because the log-Gabor filter used needed to extract features through multiple filter templates, the number of output images was large. Firstly, this paper used the Lévy flight strategy to improve the PSO algorithm to create the LPSO algorithm. The improved LPSO algorithm could successfully solve the PSO algorithm’s problem of falling into the local optimal solution. Then, the LPSO algorithm and log-Gabor filter were used to generate an adaptive filter, which could directly output the optimal results in multiple feature extraction images. Finally, a classifier based on HOG + SVM was used to identify and classify the defect types. The method extracted and identified the scratch-type, crack-type, sand-hole-type, and spot-type defects, and the recognition rate was more than 92%.

Deep Learning for Face Recognition under Complex Illumination Conditions Based on Log-Gabor and LBP

The complex light conditions, and this is one of the most important and difficult problems in practical face recognition, in this paper, we propose a new deep learning-based method to solve the problem of the effect of the light of the changes in the facial recognition process. First, the primary treatment of the lighting can be used to improve the negative effects of intensive changes in the lighting of a photo of a face, and for a second, the Log-Gabor filters in order to get the images used in the Log-Gabor features at different scales and in different directions, and then, the LBP (Local Binary Pattern) features on the image subblock is obtained. Finally, the histogram of the texture of the features of the formation and the visual layer of the deep belief network (DBN) to drop, and then the classification and the recognition is done with a deep-learning-DBN. The experimental results show that superior performance can be obtained in the application of the strategy in comparison to some of the modern technology.

Unpaved road detection using optimized log Gabor filter banks

The revised unpaved road detection system (RURD) is a novel method for detecting unpaved roads in an arid environment from color imagery collected by a forward-looking camera mounted on a moving platform. The objective is to develop and validate a novel system with the ability to detect an unpaved road at a look-ahead distance up to 40 meters that does not utilize an expensive sensor, i.e., LIDAR but instead a low-cost color camera sensor. The RURD system is composed of two stages, the road region estimation (RRE) and the road model formation (RMF). The RRE stage classifies the image patches selected at 20-meter distance from the camera and labels them to either road or non-road. The classification result is used as a high confidence road area in the image, which is used in the RMF stage. The RMF stage uses log Gabor filter bank to extract road pixels that connect to the high confidence road region and generates a 3rd degree polynomial curve to represent the road model in a given image. The road model allows the system to extend the detection range from 20 meters to farther look-ahead distance. The RURD system is evaluated with two-years worth of data collection that measures both spatial and temporal precisions. The system is also benchmarked against an algorithm from Rasmussen entitled "Grouping Dominant Orientations for Ill-Structured Roads Following", which shown an average increase detection accuracy over 30 [percent].

Finger Knuckle Print Recognition using MMDA with Fuzzy Vault

Currently frequent biometric scientific research such as with biometric applications like face, iris, voice, hand-based biometrics traits like palm print and fingerprint technique are utilized for spotting out the persons. These specific biometrics habits have their own improvement and weakness so that no particular biometrics can adequately opt for all terms like the accuracy and cost of all applications. In recent times, in addition, to distinct with the hand-based biometrics technique, Finger Knuckle Print (FKP) has been appealed to boom the attention among biometric researchers. The image template pattern formation of FKP embraces the report that is suitable for spotting the uniqueness of individuality. This FKP trait observes a person based on the knuckle print and the framework in the outer finger surface. This FKP feature determines the line anatomy and finger structures which are well established and persistent throughout the life of an individual. In this paper, a novel method for personal identification will be introduced, along with that data to be stored in a secure way has also been proposed. The authentication process includes the transformation of features using 2D Log Gabor filter and Eigen value representation of Multi-Manifold Discriminant Analysis (MMDA) of FKP. Finally, these features are grouped using k-means clustering for both identification and verification process. This proposed system is initialized based on the FKP framework without a template based on the fuzzy vault. The key idea of fuzzy vault storing is utilized to safeguard the secret key in the existence of random numbers as chaff pints