<![CDATA[Fire hazards in indoor industrial environments require fast and reliable detection systems, as conventional sensor-based methods often suffer from delayed responses and high false-alarm rates. This study proposes a low-cost, Internet of Things-integrated visual fire detection system based on the YOLOv11 deep learning model implemented on an Orange Pi 4A. The system integrates an IP camera for visual acquisition, real-time detection, and automatic data logging through a MySQL-based monitoring platform. Experiments were conducted in a 3 × 3 m indoor environment using candle, stove, and burning fires at various camera distances. System performance was evaluated using confidence score, bounding box pixel area, and recall based on True Positive and False Negative classifications. Candle flames were reliably detected up to 100 cm with recall values of 90.24%–100% and pixel areas below 5,000 px, while stove flames achieved recall above 93% at 50–100 cm with pixel areas of 11,144–42,525 px. Burning fires maintained high performance up to 300 cm, reaching confidence values above 0.70 and recall rates of 78.94%–100% with pixel areas exceeding 44,000 px. The results indicate that detection reliability is primarily influenced by apparent flame size rather than camera distance. Overall, the proposed system demonstrates strong feasibility as an embedded, IoT-integrated fire detection solution for early warning in indoor industrial environments, although limitations remain in detecting small flames under low-resolution and low-light conditions.]]>
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