Automated Floor Cleaning System for Hedgehog Cages using The You Only Look Once (YOLO) Algorithm for Waste Detection

African pygmy hedgehogs (Atelerix albiventris) have gained popularity as exotic pets; their reliance on olfactory and auditory senses makes them highly sensitive to environmental changes, particularly poor cage hygiene. Unsanitary conditions may cause odor accumulation, microbial growth, and parasitic infestations, leading to stress, reduced appetite, digestive disorders, infections, and respiratory issues. This study presents the design and evaluation of an automated cage floor cleaning system for hedgehogs based on computer vision using the You Only Look Once (YOLO) algorithm. The proposed system performs precise waste detection, autonomous cleaning, and remote monitoring and control via a mobile application. The cage consists of six PETG floor plates rotated by 180° using six MG996R servo motors, while a 12V submersible pump performs rinsing. Processing is conducted on a Raspberry Pi 4B with a PCA9685 PWM driver, integrated with Google Cloud/Firebase and a Flutter-based mobile application. Real-time video streaming is implemented using WebRTC and decoupled from the detection pipeline to maintain a stable frame rate. A YOLOv11s model was trained for object detection, achieving a precision of 0.785, recall of 0.753, and [email protected] of 0.802 ([email protected]–0.95 of 0.491). Hedgehog detection achieved high performance (AP 0.969), while waste detection showed lower performance (AP 0.676) due to visual similarity with wooden pellets. Experimental results demonstrate smooth real-time streaming (23–27 FPS), low control latency, and reliable automated cleaning with a safety interlock mechanism that prevents cleaning when a hedgehog is detected. The system is feasible for automated cage maintenance, with future improvements focusing on dataset enhancement and system optimization.