Solar cells are the key part of photovoltaic power system, and the online quality inspection of solar cells ensures conversion efficiency and usable lifetime of photovoltaic modules. However, automatic crack detection in EL images of solar cells has been a challenging task, owing to the heterogeneously textured background, low contrast between crack and surrounding background including randomly distributed crystal grains, the diversity of crack types, and so on. To address these challenges, this paper presents a new accurate and robust crack detection scheme for multicrystalline solar cells. Firstly, a novel steerable evidence filter is developed to generate the crack saliency map, which significantly enhances the contrast between crack and surrounding background. Secondly, a segmentation-based method including local threshold and minimum spanning tree is employed, which ensures the extraction of complete crack. Next, the crack can be accurately located in the inspection image by computing the crack skeleton. Finally, experimental results on defective and defect-free EL images show that the proposed scheme is accurate and robust, and various types of cracks can be correctly detected. The proposed scheme achieved average detection rate of 94.4% on the data set, which performs better than the previous methods.