<![CDATA[Bird strike incidents in airport airside areas pose a high-risk safety threat. Current conventional mitigation efforts rely on open-loop acoustic alarm devices that operate continuously for 24 hours. This method causes massive electrical energy waste and triggers habituation phenomena in target wildlife. This research aims to design a smart bird deterrent system prototype based on artificial intelligence utilizing a closed-loop control architecture. The methodology integrates the YOLOv8 Nano computer vision algorithm as a visual feedback sensor unit, deployed on low-end Edge AI computing (Intel Celeron N3350). To prevent CPU computational bottlenecks, the model is optimized into an Intermediate Representation format using Intel OpenVINO. The audio actuator trigger signal is strictly executed only when the bird detection accuracy exceeds a 0.5 confidence threshold. Test results indicate that the algorithm dynamically responds to wildlife presence, precisely halting audio emissions when the area is clear, effectively solving the habituation problem. The OpenVINO framework optimization successfully accelerated the image processing computational rate from 0.80 FPS to a stable 1.30 FPS. The system proved reliable with a 0.77-second response latency, offering an innovative control engineering output for energy efficiency in aviation facilities.]]>
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