Evaluation of deep learning architectures for wildlife object detection: A comparative study of ResNet and Inception
PositiveArtificial Intelligence
- A comparative study evaluated the effectiveness of deep learning architectures ResNet-101 and Inception v3 for wildlife object detection, achieving a classification accuracy of 94% and a mean Average Precision of 0.91 with ResNet-101. This research highlights the challenges of environmental variability and visual similarities among species in wildlife monitoring.
- The development of these deep learning models is significant for biodiversity conservation and ecological monitoring, as they enhance the ability to accurately identify and track wildlife, which is crucial for habitat protection efforts.
- The emergence of AI tools like ShadowWolf, which automates the labelling and evaluation of wildlife images, underscores the growing importance of technology in wildlife monitoring. Such advancements are vital as human expansion increasingly threatens biodiversity, necessitating innovative solutions for effective conservation.
— via World Pulse Now AI Editorial System
