Human object detection and classification system based on thermal cameras using the YOLOv11 object detection model

Abstract

Strategic institutions such as military campuses, defense research centers, and government facilities face increasingly complex security challenges, particularly in environments with low visibility and limited manual patrol capabilities. Conventional surveillance systems often perform poorly in dark environments because they depend heavily on visible light. Therefore, this research proposes a human object detection and classification system based on thermal cameras integrated with the YOLOv11 object detection model. Thermal cameras are capable of capturing heat radiation emitted by objects, enabling effective visualization under low-light and completely dark conditions. The proposed system combines thermal imaging technology with the real-time detection capability of YOLOv11 to automatically identify and classify human objects. This research employs the Research and Development (R&D) method, including dataset collection, image annotation, data augmentation, data preprocessing, model training, and system evaluation. The dataset consists of thermal images enhanced using augmentation techniques such as cropping, rotation, brightness adjustment, and blur effects to improve model robustness. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix analysis. Experimental results demonstrate that the proposed system achieved an average accuracy of 86.36%, with accuracy values of 85.98% under completely dark conditions and 86.75% under dim-light conditions, indicating that the model is capable of reliably detecting and classifying human objects in low-visibility environments. These findings show that the integration of thermal cameras and YOLOv11 can contribute to the development of intelligent security systems that improve surveillance efficiency while reducing dependence on manual monitoring.