Development of a Deep Learning-Based Object Detection Model Optimized for Edge Devices Using Thermal Imaging Data
Küçük Resim Yok
Tarih
2026
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Thermal imaging has become an effective alternative to RGB vision for human detection in low-light, night-time, and visually challenging environments. Despite its robustness to illumination changes, deploying deep learning-based object detection models on resource-constrained edge devices remains challenging due to high computational and memory requirements. In this study, we investigate the performance and deployability of YOLO-based object detection models for thermal human detection under edge-oriented constraints.YOLOv5 and YOLOv8 architectures are trained and evaluated using thermal infrared images from the LLVIP dataset, with a focus on detection performance, inference speed, and model size. Experimental results show that YOLOv5 achieves slightly higher mAP@0.5 than YOLOv8 (0.983 vs 0.971) while offering significantly better computational efficiency, making it more suitable for edge deployment. Based on this observation, the YOLOv5 model trained on thermal data is selected for further optimization.The selected model is exported to the ONNX format and optimized using NVIDIA TensorRT with FP16 and INT8 precision modes. Performance evaluations demonstrate that FP16 optimization significantly reduces inference latency without degrading detection performance, enabling real-time processing at approximately 170 frames per second. In contrast, INT8 quantization does not yield the expected speed or memory benefits under the tested conditions, mainly due to mixed-precision execution and batch size limitations.The results indicate that FP16-optimized YOLOv5 offers a reliable and efficient solution for real-time thermal human detection on edge-oriented hardware, providing practical design insights for deploying thermal vision systems in security and surveillance applications. © 2026 IEEE.
Açıklama
5th International Conference on Informatics and Software Engineering, IISEC 2026 -- 5 February 2026 through 6 February 2026 -- Ankara -- 221523
Anahtar Kelimeler
edge computing, human detection, model optimization, TensorRT, Thermal imaging, YOLOv5, YOLOv8
Kaynak
Proceedings - 5th International Conference on Informatics and Software Engineering, IISEC 2026
WoS Q Değeri
Scopus Q Değeri
N/A
