Evaluating YOLOv8 for On-Device Person Detection: Performance and Efficiency on Android Smartphones

Due to limited hardware, consumer-grade surveillance cameras usually rely on cloud-based computer vision models to detect people in video footage. However, this approach introduces a recurring cost, as users must continuously pay for cloud processing. One possible solution is to use mobile devices for person detection, as they can be found in most households. Yet, experimental evaluations on the impact of different computer vision models on mobile device resource usage are limited. This study examines the efficiency of YOLOv8 models on Android devices, assessing detection performance, inference time, memory consumption, and energy efficiency. The models were tested using two machine learning frameworks, LiteRT (formerly TensorFlow Lite) and ONNX Runtime, to determine the most suitable approach for mobile inference. Experimental results confirm that compact models, such as YOLOv8n and YOLOv8s, offer the best trade-off between computational efficiency and detection accuracy, while LiteRT outperforms ONNX in all evaluated metrics.