Design of Animal Detection and Control System Based on Image Processing and Ultrasonic Sound Device

In this study, an integrated system combining image processing and ultrasonic sound technologies is proposed to remove unwanted animals from certain areas without causing harm. Using a deep learning-based object recognition algorithm (YOLOv11), animal species such as pigs, dogs and pigeons are detected in real-time with high accuracy. After detection, ultrasonic frequencies specific to each species - 13 kHz for pigeons, 22 kHz for dogs, 29 kHz for pigs - are transmitted to the environment to remove the animals without harming them. The system was trained with opensource datasets and images obtained from the field, and prototyped to run on Raspberry Pi 4B considering portability and energy efficiency. In field tests conducted under real-life conditions, the detection accuracy of the system was measured as 95.5% with mAP@0.5, while the removal success was above 85% on average. With its real-time response time (40 ms) and species-based frequency adjustment, the system offers an ethical, low-cost and environmentally friendly solution that can be used in agriculture, urban security and industrial areas.