Wild Animal Recognition Using an Edge Device

Neural networks and deep learning are in rapid development; new technologies and increasingly high-performance computing centres allow operations to be carried out that, until a few years ago, were impossible due to excessive processing times. The possibility of exploiting an Edge/Cloud configuration allows speed in training neural networks, ability to operate in real-time, elasticity, and resistance of the entire system in the event of a failure. This configuration helps reduce costs by using less expensive tools in the sensing and edge regions. In this work, an edge computing system was implemented to recognise wild animals such as goats and wild boars. The image recognition problem was therefore addressed by exploiting transfer learning techniques on two state-of-the-art methods: YOLOv5 and EfficientNet. The comparison of the results highlighted the pros and cons of the two methods.