Akademik Veri Yönetim Sistemi
8th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2026, Ankara, Türkiye, 21 - 23 Mayıs 2026, (Tam Metin Bildiri)
Missile warning systems play a critical role in ensuring the survivability of airborne platforms by enabling early detection of incoming threats. Due to low signal-to-noise ratios, dynamic backgrounds, and the small size of targets in the early stages, missile plume detection in passive electro-optical approaches is still difficult. Solar-blind ultraviolet (SBUV) sensing offers improved target-to-background contrast under daylight conditions, making it a suitable modality for missile plume detection. This study assesses different You Look Only Once (YOLO) based object detection architectures (YOLOv5, YOLOv8, YOLOv10, and YOLOv11) for missile plume detection using a synthetic SBUV dataset in order to assess the models' detection performance and computational efficiency. They are trained and compared under the same conditions. To provide a thorough analysis, standard evaluation metrics, which are precision, recall, f1 score and mean average precision (mAP) are combined with measurements of inference speed. The results indicate that all evaluated models achieve high detection performance, while notable differences emerge in inference speed and robustness across varying confidence thresholds. Although newer architectures such as YOLOv10 and YOLOv11 introduce more advanced design components, they do not consistently provide lower end-to-end latency under the evaluated conditions. In contrast, YOLOv8 achieves the lowest inference time, with YOLOv5 demonstrating comparable performance and more stable behavior across confidence thresholds. Furthermore, confusion matrix analysis reveals that newer YOLO models reduce background-related false detections compared to YOLOv5, indicating improved discrimination capability in complex scenes. Overall, the findings confirm that YOLO-based approaches are well-suited for SBUV-based missile plume detection, and that recent architectural developments primarily enhance robustness and detection quality rather than consistently reducing inference latency in practical deployment scenarios.