Abstract: Deer-vehicle collisions represent a critical safety challenge in the United States, causing nearly 2.1 million incidents annually and resulting in approximately 440 fatalities, 59,000 injuries, and $10 billion in economic damages. This poster presents a real-time detection and driver warning system that integrates thermal imaging, deep learning, and cellular vehicle-to-everything (C-V2X) communication to help mitigate deer-vehicle collisions. Our system was trained and validated on a custom dataset of over 12,000 thermal deer images collected in Mars Hill, North Carolina. The system was field tested during a follow-up visit to Mars Hill and readily sensed deer across diverse weather conditions, with thermal imaging maintaining 88-92% detection accuracy in challenging scenarios. When a high probability detection threshold is reached sensor data sharing messages are broadcast to surrounding vehicles and/or roadside units via C-V2X to alert drivers of deer in their vicinity. This research establishes a viable technological pathway for reducing deer-vehicle collisions through thermal imaging and connected vehicles.