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 morpho...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.展开更多
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 estima...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.展开更多
To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and ...To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and then for the one-dimensional Otsu algorithm, in order to improve the accuracy of the algorithm, then it puts forward a two-dimensional Otsu algorithm. Finally the two-dimensional Otsu algorithm combined with the one-dimensional Otsu algorithm and the improved Otsu algorithm. By analyzing the improved Otsu algorithm, this paper considers the pixel gray value, neighborhood information, excluding light, noise and the relative efficiency of one-dimensional Otsu algorithm higher accuracy. The relative dimensional Otsu algorithm operating efficiency has been greatly improved. Improved Otsu algorithm in dealing with boiler ash ratio detection has played a very good part in the ecological environment, economic development and some other important aspects.展开更多
碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎...碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎米。将检测结果与国标法进行对比,结果表明逻辑回归模型的曲线线下面积(area under the curve,AUC)值为0.987,柯尔莫可洛夫-斯米洛夫(Kolmogorov-Smirnov,KS)值为0.909,0.5为最佳阈值;而国标法的AUC值为0.922,KS值为0.669,21为最佳阈值。该文所建立的逻辑回归模型的准确率、精确率、召回率及F1分数均高于国标法。此外,逻辑回归模型的AUC值比国标法的AUC值更接近于1,KS值也更高,表明逻辑回归模型能够更好地区分碎米与整米。长轴(x_(1))、面积(x_(2))、短轴(x_(3))与长短轴比(x_(4))4个特征参数都是模型中具有显著影响的因素,对应的线性关系为z=-139.97-5.35x_(1)+10.93x_(2)+2.86x_(3)+34.59x_(4)。展开更多
基金supported by Natural Science Foundation of Jilin Province(YDZJ202401352ZYTS).
文摘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.
基金supported by the National Natural Science Foundation of China (61302188)the Nanjing University of Science and Technology Research Foundation (2010ZDJH05)
文摘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.
文摘To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and then for the one-dimensional Otsu algorithm, in order to improve the accuracy of the algorithm, then it puts forward a two-dimensional Otsu algorithm. Finally the two-dimensional Otsu algorithm combined with the one-dimensional Otsu algorithm and the improved Otsu algorithm. By analyzing the improved Otsu algorithm, this paper considers the pixel gray value, neighborhood information, excluding light, noise and the relative efficiency of one-dimensional Otsu algorithm higher accuracy. The relative dimensional Otsu algorithm operating efficiency has been greatly improved. Improved Otsu algorithm in dealing with boiler ash ratio detection has played a very good part in the ecological environment, economic development and some other important aspects.
文摘碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎米。将检测结果与国标法进行对比,结果表明逻辑回归模型的曲线线下面积(area under the curve,AUC)值为0.987,柯尔莫可洛夫-斯米洛夫(Kolmogorov-Smirnov,KS)值为0.909,0.5为最佳阈值;而国标法的AUC值为0.922,KS值为0.669,21为最佳阈值。该文所建立的逻辑回归模型的准确率、精确率、召回率及F1分数均高于国标法。此外,逻辑回归模型的AUC值比国标法的AUC值更接近于1,KS值也更高,表明逻辑回归模型能够更好地区分碎米与整米。长轴(x_(1))、面积(x_(2))、短轴(x_(3))与长短轴比(x_(4))4个特征参数都是模型中具有显著影响的因素,对应的线性关系为z=-139.97-5.35x_(1)+10.93x_(2)+2.86x_(3)+34.59x_(4)。
