| 基于红外图像处理技术的钢构件损伤识别 |
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| 引用本文： | 邹兰林, 许瀚文, 周兴林. 基于红外图像处理技术的钢构件损伤识别[J]. 红外技术, 2020, 42(3): 286-293. |
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| 作者姓名： | 邹兰林 许瀚文 周兴林 |
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| 作者单位： | 武汉科技大学汽车与交通工程学院,湖北武汉,430065 |
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| 基金项目： | 国家自然科学基金;国家重大科研仪器研制项目 |
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| 摘 要： | 针对现有红外图像处理算法在处理桥梁钢制构件损伤图像时信噪比差,对比度低,分辨率低,图像细节丢失,边缘模糊,损伤识别精准度差等问题,本文提出空域滤波与时域滤波结合的红外图像增强算法,以弥补现有算法不足,从多方位抑制图像背景噪声,增强图像细节信息,强化损伤边缘轮廓,实现钢构件损伤部位精准识别与提取,并结合清晰度,对比度,峰值信噪比,均方误差四大指标对处理结果进行定量评价,评价结果表明基于高频强调滤波与非线性灰度转换结合的红外图像增强算法切实可行,且针对红外图像检测下的钢构件损伤识别效果显著.
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| 关 键 词： | 图像处理 高频强调滤波 非线性灰度变换 损伤识别 |
Damage Identification of Steel Members Based on Infrared Image Processing Technology |
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| ZOU Lanlin, XU Hanwen, ZHOU Xinglin. Damage Identification of Steel Members Based on Infrared Image Processing Technology[J]. Infrared Technology , 2020, 42(3): 286-293. |
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| Authors: | ZOU Lanlin XU Hanwen ZHOU Xinglin |
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| Affiliation: | (School of Automotive and Transportation Engineering,Wuhan University of Science and Technology,Wuhan 430065,China) |
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| Abstract: | In view of the existing infrared image processing algorithms in dealing with damage images of bridge steel members,the signal-to-noise ratio is low,the contrast is low,the resolution is low,the image details are lost,the edges are blurred,and the accuracy of damage recognition is poor.We propose a spatial and time domain filtering combined infrared image enhancement algorithm to improve existing algorithms,suppressing image background noise from multiple directions,enhancing image detail information,strengthening damage edge contours,accurately identifying and extracting damaged parts of steel components.The processing results are quantitatively evaluated by clarity,contrast,peak signal-to-noise ratio(PSNR)and mean square error.The evaluation results show that the infrared image enhancement algorithm based on high-frequency emphasis filtering and nonlinear gray-scale conversion is feasible.The damage recognition effect is remarkable for the steel components under infrared image detection. |
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| Keywords: | image processing high frequency emphasis filtering nonlinear gray scale transformation damage recognition |
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