Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm.First,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image.The algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and background.Afterwards,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm.Finally,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is increased.Combined with the adaptive iterative threshold algorithm,the threshold selection process is further fine-tuned.Experimental results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates.The average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.

改进沙猫群优化算法的2D-OTSU多阈值图像分割

针对传统多级阈值图像分割方法精度低、收敛速度慢的问题,提出一种改进的沙猫群优化算法(Improved Sand Cat Swarm Optimization, ISCSO)用于全局优化,并应用于2D-OTSU多阈值图像分割任务。通过使用Henon混沌映射和反向折射机制初始化种群,使得种群的分布更加均匀,提高搜索的起始状态,从而增加算法的全局搜索能力;采用非线性灵敏度更新公式来平衡搜索多样性和收敛精度;引入可变螺旋搜索策略改进位置更新算法,以确保算法具有较好的搜索多样性和跳出局部最优解的能力。选取6张测试图像对ISCSO算法进行2DOTSU多阈值图像分割实验,采用峰值信噪比(PSNR)、特征相似性指数(FSIM)和结构相似性指数(SSIM)对实验结果进行评价。实验结果表明,基于ISCSO算法的2D-OSTU多阈值图像分割方法在图像分割任务中85.2%的结果优于对比算法,具有较强的搜索精度和收敛速度,这证明了ISCSO算法在图像分割领域的有效性和潜力。

Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.

Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning

To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.

基于分块Otsu法的车窗开关缺陷阈值分割方法

车窗开关的外观与性能有着不可分割的关系,表面缺陷检测是阻止缺陷产品流入市场的重要手段。针对用传统的阈值算法对车窗开关进行分割时容易造成误分现象,提出一种分块Otsu阈值法。对开关组图像去噪后分割开关,然后对开关图像进行分块阈值处理,通过提取特征进行缺陷判断。分块阈值处理是将图像分块,以整幅图像灰度最高值作为基准,对每块图像进行灰度补偿,并进行Otsu阈值法处理。实验结果表明:分块Otsu法能有效分割出车窗开关的缺陷,同时消除了光照不均现象的影响,充分验证该方法的可行性;利用多种常见阈值分割处理进行对比实验,其效果优于其他处理方法,充分验证方法的高效性。

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

三维直方图重建和降维的Otsu阈值分割算法

针对三维Otsu阈值分割算法中因区域误分而产生的抗噪性差这一问题,提出了一种三维直方图重建和降维的Otsu阈值分割算法.该方法首先在详细分析三维直方图中噪声点分布的基础上,通过重建三维直方图,减弱了噪声干扰;然后将三维直方图区域划分由八分法改为二分法,使得阈值搜索的空间维度从三维降低到一维,减少了处理时间和存储空间.本文最后给出了算法的分割结果和运行时间,并与三维Otsu方法、二维分解法和二维斜分法进行对比.实验结果表明,本文算法的抗噪性更强,且分割效果更为理想,同时时间复杂度也远低于三维Otsu法.

Otsu算法在Canny算子中的应用

Canny算子应用于细胞图像分割,只要选择适当的参数就可以取得好的分割效果。在分析细胞图像直方图后,利用类间方差最大化阈值分割算法(Otsu)可以实现Canny算子中门限值的自适应选择。试验结果表明,Otsu算法应用于Canny算子中的门限选择,改善了算法在细胞图像中的分割效果,提高了算法的自适应性能。

基于积分图像的快速二维Otsu算法

Otsu自适应阈值算法是图像分割的经典方法之一,在其基础上发展起来的二维Otsu阈值算法却因为计算复杂而制约了其应用。本文针对二维Otsu的耗时瓶颈问题,引入积分图像简化了二维直方图最佳阈值搜索过程的计算复杂度,从而大大缩短了算法处理时间。实验结果表明,本文提出的算法在分割效果上与原始算法完全相同,但计算时间远远小于后者,具有很好的实时性。

二维直方图准分的Otsu图像分割及其快速实现

传统二维Otsu法主要由于对二维直方图采用主对角线区域概率和近似为1的假设等原因,以致分割结果不够准确.针对此问题,提出了一种二维直方图准分的Otsu快速图像分割方法.(1)准确选择邻域模板构建二维直方图并将Otsu阈值法用于此直方图上以便提高分割性能;(2)对二维直方图主对角线上的目标和背景两区域的Otsu公式中对应量准确取值使阈值选取更准确;(3)对二维直方图投影进行分析得到其特性,并证明三个定理的存在,利用此特性和三个定理导出新型、快速的递推算法来降低计算复杂度.实验结果表明:与当前二维Otsu阈值法相比,本文提出的方法分割更准确和抗噪性更强,而且其运行时间少,与当前二维Otsu斜分递推算法的运行时间相近.

扩展的Otsu最优阈值图像分割的实现方法

基于付忠良等人提出的扩展的Otsu最优阈值图像分割方法,提出了遗传算法的解决方案,并给出了遗传算法中基本参数的设定.基于图像的像素方差信息,利用遗传算法全局搜索图像的单阈值和双阈值,这样不但缩短了计算时间,而且具有遗传算法鲁棒性和自适应的特点,比传统的Otsu方法有明显的优点.

基于显著性的OTSU局部递归分割算法

针对OTSU方法的局限性 ,提出了一种基于显著性的OTSU局部递归分割算法 .该算法的思路是把一次OTSU分割得到的目标区域作为新的图像进行下一次OTSU分割 ,依次递归下去 ,直到满足一定条件为止 ,然后把每次分割得到的目标区域按照一定加权原则累加起来 ,得到分割结果图像 .实验结果表明 ,在不同的冷背景下 ,基于显著性的OTSU局部递归分割算法能够稳定地实现目标分割提取 。

基于并行遗传算法的Otsu双阈值医学图像分割

传统遗传算法用于搜索某些函数极值时精确度较低且稳定性较差。针对该问题,提出了一种基于并行遗传算法的Otsu双阈值医学图像分割算法。在该算法中,进化在多个不同的子群中并行进行,避免单种群进化过程中出现的过早收敛现象,提高整个算法的收敛速度。100次阈值计算实验结果表明,提出的分割算法与传统遗传算法相比,不仅能够对图像进行准确的分割,而且具有更强的精确性和稳定性。其收敛速度明显优于基于单种群的遗传算法的Otsu双阈值医学图像分割。

一种新的自适应二维Otsu图像分割算法研究

现有二维Otsu图像分割算法的阈值识别函数是通过计算类间离散度矩阵的迹来实现的,没有考虑目标和背景这两类像素自身的内聚性且计算复杂度高,为此提出一种新的阈值识别函数设计算法.该算法先统计待分割图像目标类和背景类各自类内的绝对差,相加得到总体类内绝对差之和;再统计目标类和背景类两类之间的总体平均离差;然后把总体类内绝对差和类间总体离差的商式作为阈值识别函数.实验结果表明,与现有的识别函数相比,利用新构造的阈值识别函数来自适应寻优阈值,从主观上和客观上都取得了较好的分割效果,而且计算量较小.

基于分解的三维Otsu图像分割快速算法

针对三维Otsu图像分割算法计算复杂度高、运算量大的问题,提出一种基于分解的三维Otsu图像分割快速算法。首先将三维Otsu分解为三个一维Otsu;然后,在分析一维Otsu的基础上,结合类间距离和类内距离,提出一种新的阈值识别函数设计算法,并给出了快速实现方法。实验结果表明,该算法不仅可以取得较好的分割效果,而且计算量较小,比三维Otsu阈值分割递推算法快1400倍左右。

一种适用于SAR图像的2维Otsu改进算法

Otsu法是图像阈值分割中的经典算法之一,在图像处理中得到广泛的应用。针对原始2维Otsu法直方图区域划分上的缺陷和运算速度慢的缺点,分析了适用于SAR图像相干斑乘性噪声的直方图区域划分方法,提出了一种更符合实际图像模型的阈值选取准则。实验结果表明,该改进算法的分割效果良好,运算速度也有很大提高。

基于遗传算法的二维Otsu算法改进

为改善传统二维0tsu阈值分割算法处理图像时计算复杂度高、实时性差等缺点,将遗传算法应用到二维Otsu灰度图像阈值寻优中,并提出一种改进的自适应遗传算法。实验证明,新的算法对灰度图像有较好的分割效果,与传统算法相比,分割图像清晰,实时性也得到了明显的改进。

基于小概率策略的Otsu图像分割方法

Otsu自适应阈值法是一种经典的图像阈值分割方法,在其基础上发展起来的2维Otsu法及其改进算法由于存在计算(或空间)复杂度较高、抗噪能力差、难以扩展到多阈值等不足而制约了其应用。该文针对2维Otsu法的不足,将噪声点的出现视为小概率事件,用噪声点的邻域均值代替其灰度值,将噪声点转换为目标(或背景)像素,减少了图像中的噪声点数量;继而直接采用1维Otsu法进行分割,以较小的代价获得良好的分割效果。算法分析及测试实验表明:与现有2维Otsu法相比,该算法在复杂度、抗噪性、多阈值扩展性等方面都有明显改善。

基于改进的Otsu算法的遥感图像阈值分割

传统的Otsu算法仅仅适用于目标与背景分布均匀的图像,在处理遥感图像时具有一定的局限性。本文在分析传统的Otsu算法原理的基础上,结合遥感图像灰度级多、信息量大和边界模糊的实际情况,提出了一种改进的Otsu算法,用图像的方差信息代替均值信息计算最佳的分割阈值,实现遥感图像阈值分割。实验仿真结果表明,与传统的Otsu算法以及其它的一些改进的Otsu算法相比,本文算法具有明显的优越性。