Detection and classification of animal crossings on roads using IoT-based WiFi sensing

Road traffic accidents involving animals cause great health, environmental and monetary costs every year, specially on rural areas. Current animal detection systems suffer from either cost, scalability or accuracy issues, which prevent their effective use in a more extensive manner. To provide an accurate and cost-effective detection system, we employ WiFi sensing using low-cost IoT devices to train a modern deep learning model - the Transformer network. Our Transformer network detects road crossings with an accuracy of 97.1 percent and exhibits low false positive and false negative rates. In particular, our model accurately distinguishes vehicles from small and large animals, which enables scalable and economical accident prevention over large distances in rural roads. Finally, our model is on par with state-of-the-art predictive models, outperforming recent AutoML mechanisms and evidencing the suitability of the Transformer network for WiFi sensing-based tasks.