Real-time Animal Detection and Alert System using IoT and Deep Learning

Abstract

One pressing concern facing nations worldwide today is the incidence of fatalities and injuries resulting from road accidents. Human-animal collisions stand out as a prominent cause of many fatal accidents. Road accidents occur with alarming frequency every few seconds across the globe. The core objective of our project is to detect animals and issue warnings to vehicles, thereby mitigating highway accidents involving animals. Leveraging IoT technology, the implementation of a detection and alert system designed to significantly reduce animal-related accidents is proposed here. This alert system is engineered to transmit signals that promptly notify highway drivers when a detection event is triggered. IoT technology takes center stage in our solution, with the Raspberry Pi 3 Model B serving as the cornerstone for animal detection and vehicle alerting. Employing sensors, we identify obstacles that activate the Pi Camera, configured by the Raspberry Pi, to capture real-time images and video footage of animal movements using image detection techniques. Subsequently, individuals and vehicles navigating forested highways are issued alerts. Additionally, during nighttime hours, lights to mark specific distances are employed. Ultimately, the paper's primary objective is to safeguard animals from accidents and protect countless lives from potential danger. To achieve this, the YOLO (You Only Look Once) algorithm is implemented, which boasts the capability to process 45 frames per second and analyze the entire image to predict object presence.