在检测铁氧体电感表面微裂纹时，传统机器视觉检测存在裂纹成像信噪比不高、准确率低等问题。为此文中搭建了一套基于线激光正交扫描的微裂纹热成像检测系统。通过热像仪记录试样表面温度变化并成像；采用次最大值滤波消除热成像图的非均匀性和边缘轮廓干扰，再利用多方向扇形滤波得到试样在不同方向上的灰度图；最后通过BP神经网络和形态学处理实现对电感表面微裂纹的定性检测。结果表明：基于两种规格共610个样品测试，所有裂纹和隐裂均正确成像；自动识别算法误检率5%，裂纹漏检率6%，隐裂漏检率10%。系统每5 s检测20~35个电感，可应用于生产中自动化品质检测。

In the detection of microcrack on the surface of ferrite inductors, traditional machine vision detection has problems such as low signal-to-noise ratio and low detection accuracy. In order to solve these problems, a microcrack thermography detection system based on line laser orthogonal scanning was built. The surface temperature change of the sample was recorded by the thermal imager and imaged. Sub-maximum filtering was used to eliminate non-uniformity and edge contour interference of the thermographic image. And the multi-directional fan-shaped filtering was used to obtain the grayscale image of the sample in different directions. Finally, qualitative detection of microcracks on the surface of the inductor was realized by BP neural network and morphological processing. The results show that all cracks and microcracks are correctly imaged based on 610 samples in two specifications. The automatic identification algorithm has a false detection rate of 5%, a crack miss detection rate of 6%, and a microcrack detection rate of 10%. The system detects 20 to 35 inductors every 5 s and can be used for automated quality inspection in production.