Backgroundand Purpose: The evolution of object detection methodologies has shifted from conventional approaches to modern deep learning architectures to overcome limitations in handling object variations. The goal was to introduce YOLO, a groundbreaking architecture enabling real-time object detection, leading to the development of an AI camera using YOLO for thermal imaging object recognition. This innovation aimed to precisely identify animals, humans, and plants in thermal images while providing a user-friendly experience. MethodologyTraditional methods' limitations were highlighted, emphasizing YOLO's single-pass architecture and its integration into the AI camera system, aligning with Human-Computer Interaction principles. This AI camera system can detect object between Animals, Humans and plants. Stratified K-Fold Cross-Validation was utilized to refine the AI model, ensuring accuracy and generalization by representing object classes proportionally. ResultsEmploying Stratified K-Fold Cross-Validation achieved an overall 97.55% detection accuracy and 85.75% sensitivity, showcasing the AI model's robustness. Comparative analysis with R-CNNs revealed YOLO's 18% accuracy improvement, affirming its superiority and strategic implementation in the AI camera system. These findings validated YOLO's efficiency in thermal image object detection. ConclusionThe conclusion highlights the profound impact of YOLO, specifically within the domain of thermal image recognition, substantiating its incorporation into the AI camera system in adherence to Human-Computer Interaction (HCI) principles. The commendable precision of YOLO, coupled with its real-time capabilities and user-centric design, has resulted in the development of an advanced thermal image object detection system, representing a notable advancement in the field of object recognition technology.