Hyperparameter optimization and validation of a deep learning instance segmentation model for real-time specific military equipment surveillance
Year: 2026
Type: article
Abstract: Introduction/Objective: On the modern battlefield, a soldier's combat readiness is directly dependent on the proper use of specific military equipment (SME). Automatic verification of SME usage, primarily ballistic helmets and combat vests, is a critical factor for risk reduction and increasing combat effectiveness. Traditional object detection (bounding box) is inadequate for military conditions due to camouflage and occlusion. The objective of this paper is the development, rigorous optimization, and validation of an efficient system for real-time SME segmentation, based on a modern, lightweight YOLO12-seg architecture. Methods: The system was trained on a custom-created "Mil-SME-Seg" dataset focused on military personnel. An advanced set of data augmentations (Mosaic, MixUp, HSV-shift) was applied to achieve robustness. A systematic optimization of the key "number of epochs" hyperparameter was performed through five independent, repeatable training runs (100, 120, 150, 200, and 250 epochs) to identify the optimal balance between accuracy and overfitting. Performance was evaluated using a comprehensive set of segmentation metrics, including mAP50-95(M), mAP50(M), mAP75(M), and F1-Score(M). Results: Experiments showed that training for 150 epochs yields the best generalized results on the validation set. Peak performance was achieved at epoch 144, with an mAP50-95(M) of 0.6128, mAP50(M) of 0.7852, and an F1-Score(M) of 0.741. Metric analysis indicates that longer training runs (200 and 250 epochs) lead to clear performance degradation due to overfitting. Qualitative analysis confirms the superiority of segmentation in tactically relevant scenarios (camouflage, partial occlusion). Conclusion: The optimal number of epochs for training the model on this dataset is approximately 150. The resulting model, integrated into a system with support for dynamic "military scenarios," proved its practical applicability and tactical value, achieving real-time processing speeds (48-55 FPS). This paper lays the foundation for future systems that will use segmentation masks for equipment damage assessment and sensor fusion.
Source: Vojnotehnicki glasnik
Institution: Academy of Technical and Art Applied Studies
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204993039