Artificial Intelligence-Powered Animal Intrusion Detection for Preventing Vehicle Collisions and Agricultural Damage

Population areas located near highways, farmlands, and forest reserves show worrisome rates of wildlife activity that lead to serious threats for both vehicle drivers and animals in the region. This paper develops an economical intrusion detection system that combines Internet of Things (IoT) components alongside deep learning protocols to detect animal intrusions. The detection system combines IoT sensors with deep learning algorithms to prevent dangers through immediate alert systems and protective actions. The core component of the system uses a passive infrared (PIR) sensor as a primary motion detection device that sets off the process cycle. The PIR sensor activates the Raspberry Pi 4 Model B to start the Pi Camera so it can capture images of the detected movement. MobileNet Single Shot Multibox Detector (SSD) executes deep learning-based object detection algorithm to analyze images which understands animals together with other environmental objects in its training data. Once an animal is effectively detected in an image capture the system triggers both a buzzer sound together with light emitting diode-LED illumination. The system sends alerts simultaneously through audio and visual signals to alert drivers across the road as well as to inform local authorities. The selection of MobileNet SSD for object detection brings efficiency together with lightness to the system while maintaining 93% and higher accuracy levels, which make it suitable for low-power devices like Raspberry Pi for real-time applications. The proposed artificial intelligence (AI) system uses animal detection technologies to enhance road safety by minimizing wildlife accidents while safeguarding crops through saving farmers the cost of animal damage to their agricultural lands.