Supervised Machine Learning (ML) is used for the intent of automatic micro computed tomography (micro-CT) image segmentation of low velocity impact damage in carbon fiber reinforced polymer (CFRP) composites. Deep learning models based on the U-Net, BiSeNet, INet, and FC-DenseNet architectures were trained and refined to provide better context on the accuracy of supervised ML as compared to unsupervised ML. The unsupervised ML method relied on the statistical distances in conjunction with grayscale threshold intensity segmentation to isolate damage present in high resolution image data. Given the absence of standardization in image analysis of micro-CT data of composite materials, comparisons between supervised ML methods and unsupervised ML methods allow for investigation in the ability of deep learning models to accurately interpret various low velocity damage features.