IoT-Driven Convolutional Neural Networks for Effective Vehicle Night Vision and Image Recognition

This research introduces a novel method for improving vehicles' night vision and image recognition capabilities by combining Internet of Things (IoT) technology with Convolutional Neural Networks (CNNs). When the light level drops, traditional vehicle vision systems suffer mightily, influencing safety and efficiency. To enhance the visual data obtained from car cameras in real time, our proposed method combines IoT sensors with CNNs. Through IoT, data may be continuously collected and sent to a cloud platform, where CNNs execute complex image processing operations. Using deep learning approaches, our method greatly enhances nighttime object identification, categorization, and detection. The trial findings show that the system is more accurate and resilient at recognizing important elements, including road signs, pedestrians, and other road users, improving vehicle safety and night navigation. This research presents a scalable solution for future automotive applications by integrating into IoT and CNN technologies. It addresses the constraints of standard vehicle vision systems.