Blades are a critical part of steam turbines. Since they usually work under extremely harsh conditions, it is necessary to detect cracks that are generated during operation in time and prevent them from developing into larger ones. Crack detection is crucial to maintaining the structural health and operational safety of steam turbines. Today, one of the most common detection methods is to perform magnetic particle flaw detection manually, but it is subject to the subjective judgment of inspectors, with a low level of automation. This paper presents an automated crack detection device, which can perform magnetic particle inspection on the blades and transfer images to a host computer for further image analysis. After comparing the performance of different object detection models, yolov4 (you only look once—version 4), which is a fast and accurate real-time object detection algorithm, is chosen in this paper to extract subimages containing cracks on the host computer. Furthermore, an intelligent crack detection model is established from image processing techniques, which can be divided into four steps: image preprocessing, edge detection, crack extraction and crack length calculation. In the step of image preprocessing, a new image pyramid method is proposed to blur the background and eliminate the texture of the metal surface while keeping the cracks' information to the utmost extent. An experimental study shows a reliable performance of the proposed crack detection model.