Performance evaluation of YOLOv8 and YOLOv9 on custom dataset with color space augmentation for Real-time Wildlife detection at the Edge

Wild animals pose significant challenges in agricultural environments, necessitating effective monitoring solutions for both agricultural management and wildlife preservation. Leveraging novel deep learning algorithms for real-time animal detection holds great promise in enhancing wildlife monitoring activities. This work presents a performance evaluation of two deep-learning based image recognition models (YOLOv8 and YOLOv9) with application of color space augmentation to simulate different lightning scenarios in real-life environments. Our results show that the detection accuracy is increased by the augmented color spaces versus the natural ones, as the proposed technique is able to strengthen the model's robustness against environmental changes. Furthermore, we consider the deployment of these models on far edge devices, such as trap cameras with GPUs, where real-time analysis of wildlife activity is crucial for both management and conservation efforts.