Wrinkle defects will be inevitably produced during composite manufacturing and the in-service life of composite structures. Because of their diverse morphological changes and small deformation, it is difficult to manually identify the wrinkle with important errors. In order to improve the inspection efficiency, a Mask-RCNN algorithm is proposed to detect and classify different forms of wrinkle defects in composites based on phased array images. Carbon fiber composite laminates are prepared first in different forms of wrinkle defects. Secondly, the ultrasonic phased array is used to collect full matrix data. The longitudinal scanning image of the composite laminate is then obtained through the wavenumber imaging algorithm. According to the geometric characteristics of the folds in the geological layer, the wrinkles in the composite laminate are divided into three categories, and the relationship between the wrinkle shape and the material damage degree is established. The Mask-RCNN algorithm is finally proposed for automaticaly detecting and classifying the wrinkle defects. The introduction of semantic segmentation in this algorithm can help to reveal the positions and shapes of wrinkle defects. The experimental results show that the accuracies of Mask-RCNN in the recognition of different forms of wrinkles reach 92.1%, 90.9%, and 93.3%, respectively, and the classification and recognition of wrinkles are accurate and effective. It provides theoretical support for the integration and automation of data acquisition-imaging-defect recognition in composite industries.