International Journal of Scientific Research and Engineering Development

The introduction of computer vision and deep learning technology has transformed the field of urban infrastructure analysis, especially in the field of automated roaddetection. This study presents a strong andefficient methodology for detecting road in urban environments using a mix of OpenCV, TensorFlow, and specialized deep learning models. Using convolutional neural networks (CNNs), the suggested system trained on large datasets of urban imagery to accurately segment road from other elements such as roads, buildings, and vegetation. By integrating TensorFlow's sophisticated deeplearning capabilities with OpenCV's image processing functions, precision of roaddetection but also optimizes computational efficiency, enabling real-time apps to use it. The utilization of normalization techniques, like those offered by TensorFlow Addons, further improves model performance by ensuring consistent input data quality, which is essential to preserving high precision. in diverse environmental conditions. The procedure incorporates several important steps the implementation process: image normalization, model prediction, and result visualization. Initially, The input pictures arepreprocessed to normalize RGB values, ensuring uniformity across the dataset. Subsequently, the normalized pictures are entered into the pre-trained CNN model, which outputs a probability map showing the existence of road