Enhanced feline facial recognition: advancing cat face detection with YOLOv8 and TensorRT

In the field of computer vision, the ability to accurately detect and recognise animal features in various environments is an area of growing interest and application. This study presents an advanced cat face detection system utilising the You Only Look Once Version 8 (YOLOv8) architecture, enhanced with TensorRT optimisation for real-time processing. The approach involves a comprehensive data augmentation process to improve detection accuracy across diverse cat breeds and environmental conditions. Performance evaluation is based on quantifiable metrics; the optimised model achieves a notable reduction in inference time from 50.1ms to 0.9ms and a decrease in GPU power usage from 77 watts to 63 watts, without compromising accuracy. The accelerated processing speed and reduced power requirements make the system highly suitable for real-time applications, such as pet monitoring systems or behaviour analysis tools, where rapid and accurate detection is paramount. The research highlights the potential of deep learning algorithms in precise animal feature recognition and contributes to the field of computer vision by addressing challenges in small, diverse object detection.