Real-Time Threat Detection and Response Using Computer Vision in Border Security

International borders security is a very critical issue for all nations worldwide. Traditional methods hugely depends upon transport vehicles and also they are highly resource intensive. In this research, a sophisticated real-time threat detection and response system utilizing computer vision to bolster border security is described. The proposed model integrates high-resolution imaging, advanced machine learning algorithms of computer vision, and extensive database cross-referencing to identify and neutralize threats. By categorizing detected entities into objects, humans, and animals, the system tailors its response protocols to effectively address the unique challenges each type of threat presents. The implementation results are very promising showing a mean accuracy of 91.5% in detecting objects accurately. Also, the training and testing delay with proposed model is 0.46 seconds and 0.83 seconds respectively. Thus, our findings demonstrate the system's potential to reduce false positives and improve response times, thereby strengthening national security frameworks.