Deep Learning-Based Object Detection in Thermal Imaging
Sukriti Raj, Soumya Ranjan Pradhan, Pushpam Anand, Daksh Deswal, Syed Sajid Hussain Shah, Vinod Kumar · 2025
Thermal imaging is crucial for environmental monitoring, industrial inspection, and security, yet object detection in thermal images faces challenges like low contrast, noise, and temperature variations. This paper presents a deep learning-based approach integrating YOLOv5 and Faster R-CNN for object detection, alongside autoencoder-based anomaly detection for identifying temperature deviations. The model is trained on FLIR Thermal and KAIST Multispectral datasets, achieving a mAP of 0.973 at an IoU threshold of 0.5, with precision (0.949) and recall (0.921) outperforming conventional methods. The framework supports real-time processing, enhancing industrial safety and environmental monitoring. Future research will explore multi-modal data fusion, Vision Transformers (ViTs), and edge computing for scalable deployment. This study emphasizes the potential of deep learning to transform thermal imaging applications.