Articular cartilage, essential for smooth joint movement, can sustain micrometer-scale microcracks in its collagen network from low-energy impacts previously considered non-injurious. These microcracks may propagate under cyclic loading, impairing cartilage function and potentially initiating osteoarthritis (OA). Detecting and analyzing microcracks is crucial for understanding early cartilage damage but traditionally relies on manual analyses of second harmonic generation (SHG) images, which are labor-intensive, limit scalability, and delay insights. To address these challenges, we established and validated a YOLOv8-based deep learning model to automate the detection, segmentation, and quantification of cartilage microcracks from SHG images. Data augmentation during training improved model robustness, while evaluation metrics, including precision, recall, and F1-score, confirmed high accuracy and reliability, achieving a true positive rate of 95%. Our model consistently outperformed human annotators, demonstrating superior accuracy, repeatability, all while reducing labor demands. Error analyses indicated precise predictions for microcrack length and width, with moderate variability in estimations of orientation. Our results demonstrate the transformative potential of deep learning in cartilage research, enabling large-scale studies, accelerating analyses, and providing insights into soft tissue damage and engineered material mechanics. Expanding our data set to include diverse anatomical regions and disease stages will further enhance performance and generalization of our YOLOv8-based model. By automating microcrack detection, this study advances understanding of microdamage in cartilage and potential mechanisms of progression of OA. Our publicly available model and data set empower researchers to develop personalized therapies and preventive strategies, ultimately advancing joint health and preserving quality of life.