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The defects of solar cells (such as crack and finger failures) are commonly detected by electroluminescence (EL) methods combined with image-based artificial intelligence algorithms.In order to improve the defect detection performance of high-resolution EL images, this research introduces a modified Feature Pyramid Network (FPN). The Content Integration Module (CIM) and an Attention-Guided Module (AM) are combined to enhance the balance between the feature representation and the receptive field. A detector is also constructed by incorporating the above module into the traditional FPN, allowing the region feature network to receive multiscale features with enlarged receptive fields. The performance of the proposed detector algorithm is compared with other detectors including FPN, PANet, BAF-Detector. The paper also analyzes the issues associated with the datasets. We discussed the crack as the research object. Common mismarked conditions are identified and corrected. We note the unevenness of the crack geometry in the data set. In various geometric features, the distribution of crack length and its effect on the detection efficiency are discussed. Simulation data sets with different distributions were constructed and numerical experiments were carried out. The simulation data set method can be used to test the performance of the algorithm. The research in this paper will provide useful ideas for other defect detection fields
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