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
文摘针对单一传感器及单一蓝藻提取方法用于太湖蓝藻水华长时序监测的局限性,本文基于2014—2023年高分一号(GF-1)与Landsat 8多源影像数据,采用归一化植被指数(NDVI)方法、随机森林(RF)方法、基于最大类间方差确定样本(大津法)的随机森林(Otsu-RF)方法提取太湖蓝藻,通过对比分析确定蓝藻最优提取方法,揭示近10年太湖蓝藻水华的时空变化特征。结果表明:①Otsu-RF方法在不同影像下提取蓝藻水华的精度最高,且能够更有效地提取零星分布的蓝藻;②与GF-1图像相比,Landsat 8融合影像上的蓝藻像元纹理更加清晰,藻华提取结果更为精确;③2014—2023年太湖夏、秋季蓝藻水华爆发强度较高,春冬季较弱,其中2017、2020年太湖藻华爆发尤为严重,全域年平均蓝藻面积都超过了300 km 2;④太湖蓝藻水华春、夏、秋季多爆发在竺山湖湾、梅梁湖湾、西部湖区沿岸区域,冬季多发生在南部湖区沿岸区域。
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.
