Human pose estimation has gained significant attention in recent years for its potential to revolutionize athletic performance analysis, enhance understanding of player interactions, and optimize training regimes. Deep learning models, particularly Convolutional Neural Networks (CNNs), have outperformed traditional methods in pose estimation tasks. This study addresses a gap in sports analytics by applying two popular CNN-based frameworks, YOLO and DeepLabCut, to analyze pose estimation in hurdles athletes. Videos of a single female athlete during training sessions were used, and frames were manually annotated to capture three critical foot landmarks: ankle, heel, and big toe. The results highlight YOLOv8l’s superior accuracy, achieving a Percentage of Correct Keypoints (PCK) of 79% for these landmarks, while demonstrating the feasibility of a low-cost setup for practical applications. Visual comparisons further validate the model’s effectiveness in real-world scenarios. Additionally, YOLO predictions were utilized to analyze step progression in the time domain, providing actionable insights into athletic movement. This study underscores that even modest video equipment, combined with CNN-based methods, can equip coaches with powerful tools to analyze and optimize movements and techniques, paving the way for data-driven advancements in sports performance.

Reliable mine detection in side-scan sonar (SSS) imagery can be hindered by limited datasets. This study investigates the use of Generative Adversarial Networks (GANs) to augment scarce sonar data for improving deep learning–based mine classification. Two generative models, StyleGAN3 and FastGAN, were trained on a real SSS dataset provided by the Finnish Navy to produce synthetic samples for four-class classification tasks. The generated datasets were combined with real images at varying ratios (0–100% of synthetic data used) and evaluated across four architectures: Autoencoder, Vision Transformer (ViT), Distilled ViT, and a CNN–Transformer Hybrid. Results show that StyleGAN3 augmentation, improved average classification performance by up to 2–4% points, particularly in the 40–80% synthetic ratio range, while FastGAN data yielded inconsistent or negative effects at higher ratios. The Autoencoder achieved the highest overall average accuracy of 0.64, though its performance fluctuated considerably with different amounts of synthetic data augmentation. These findings indicate that in right conditions synthetic augmentation can enhance sonar-based mine detection, whereas lower-quality synthetic samples may degrade model reliability.