Identification of errors in the 3D printing process using computer vision applications

This thesis focuses on the integration and application of computer vision techniques in the field of 3D printing with the aim of automated detection of printing defects. The work analyzes the most common defects occurring during the 3D printing process and explores the possibilities of their identification using modern machine learning methods. A dataset for training the computer vision model was created based on data obtained from a database of images depicting various printing defects, which were subsequently categorized into specific classes. The dataset was manually annotated using the CVAT tool in COCO format, with individual defects labeled using polygonal annotations. In total, the dataset contains approximately 154 thousand annotated objects divided into five defect classes. Based on the prepared data, an object detection model utilizing the YOLOv12 architecture was trained, combining high inference speed with advanced image processing mechanisms. The model was trained using different hyperparameter settings, specifically with a fixed number of 100 epochs and a learning rate of 0.01, while the batch size was adjusted according to the input image resolution (256 for 320 px, 64 for 640 px, and 32 for 1080 px). Additionally, three input resolutions (320, 640, and 1080 px) were evaluated to optimize detection accuracy. The performance of the trained model was evaluated using standard metrics such as precision, recall, and mean Average Precision (mAP) at different IoU thresholds. The results showed that increasing the resolution led to an improvement in detection accuracy, with mAP50 increasing from approximately 0.63 (320 px) to 0.74 (640 px) and further to 0.77 (1080 px). A similar trend was observed for mAP50-95, which increased from 0.37 to 0.58, as well as for precision, which rose from 0.83 to 0.89, and recall, which improved from 0.56 to 0.73. The most significant improvement was observed when increasing the resolution from 320 px to 640 px.