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.