Vision-based black ice identification using lightweight CNN and CLAHE-enhanced imagery

This paper presents BlackNet, a vision-based black ice detection system designed for real-time vehicular safety. Unlike traditional methods that require expensive environmental sensors, BlackNet leverages existing onboard surround-view cameras. The proposed architecture integrates ResNet-style residual connections into a lightweight MobileNetV2 backbone to optimize feature extraction for subtle road surface variations. To enhance visibility in low-light and high-glare conditions, Contrast Limited Adaptive Histogram Equalization (CLAHE) is utilized for image preprocessing. The model was trained and validated on a comprehensive dataset of 15,200 images, achieving an accuracy of 92.4%. We propose a cloud-assisted deployment framework where inference is performed remotely in cloud, overcoming the computational constraints of edge devices. This approach offers a scalable, hardware-efficient solution for autonomous and connected vehicle safety.