Manufacturing of iron ore green pellet is a significant step in metallurgy industry. Crack detection for green pellet is a key step in the measuring process of the important pellet quality metric (drop strength). However, current image-based methods are mainly used to detect cracks on the flat surface of bridges, roads and solar cells. Therefore, their crack detection ability is limited on pellet with curved surface, and is easily affected by the raw material stains, pellet edge contour, strong light reflection or other interferences in pellet images, resulting in the false detection or missed detention. To solve the problem, a crack detection method for green pellet based on steerable evidence filter (SEF) is proposed. Firstly, the target area of green pellet is segmented by the active contour model, which is used to eliminate the raw material stains in the image background. In order to overcome the interfaces of pellet edge contour and strong light reflection, steerable evidence filter is used to generate the response map of pellet crack, followed by the morphological processing and connected domain analysis method used to eliminate the pellet edge response and noise in response map of pellet crack, so that more accurate crack segmentation results can be obtained. Finally, the connectivity domain method is used to detect cracks and calculate the number of cracks. In order to verify the proposed method, experimental platform was built to establish dataset of green pellet, and about 300 green pellet images with different backgrounds and the number of cracks were captured. Results show that our method outperforms five crack detection methods in crack segmentation metrics including accuracy, precious, F1. Accuracy of detecting cracks in pellet is 96%, and the accuracy of detecting the number of cracks is 90%. The crack detection results lay a foundation for the automatic and intelligent detection of drop strength quality metric of green pellet.