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As a nondestructive testing method, magnetic particle testing is widely used in power industry. The comprehensive sensitivity of the magnetic particle detection can be tested by A1 standard shims. However, at present, the evaluation of the clarity of magnetic trace on standard shims still depends on subjective judgment of the tester. In order to eliminate the subjective factors in the evaluation, this paper proposes a standard magnetic trace evaluation method based on machine vision. Based on Python programming language and OpenCV function library, the initial obtained magnetic trace is processed by image correction, magnetic trace extraction, quantitative analysis and evaluation using computer program. On this basis, the influence of the thickness of non-magnetic layer on the comprehensive sensitivity of magnetic particle detection is investigated using this method. It is shown that the magnetic trace evaluation method based on machine vision is more objective and accurate than the conventional manual evaluation.

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磁粉检测作为一种无损检测方法，在电力行业有广泛应用，其综合灵敏度由A1型标准试片检验，然而标准试片磁痕的清晰度评价仍取决于检测人员的主观判断。为排除磁痕清晰度评价中人为主观因素，提出一种基于机器视觉的标准试片磁痕清晰度评价方法。对获取的标准试片磁痕图像，使用Python程序设计语言调用OpenCV函数库，实现对磁痕图像的掩膜获取，然后通过二值化提取磁痕以及计算磁痕与背景的直方图，定量分析与评价磁痕图像的清晰度，同时使用该方法确定了非磁性层厚度对磁粉检测综合灵敏度的影响。标准试片磁痕图像分析结果可知，基于机器视觉的标准试片磁痕提取效果良好，清晰度评价结果比传统人工评价更加客观可靠。

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邱张维佳（1993），男，博士，工程师，主要研究方向为无损检测与电站材料分析，qiuzhangweijia@tpri.com.cn。

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邱张维佳（1993），男，博士，工程师，主要研究方向为无损检测与电站材料分析，qiuzhangweijia@tpri.com.cn。

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