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DEVELOPMENT OF COMPUTER VISION TECHNIQUES FOR TERRAIN ANALYSIS
Terrain analysis has become an important application of computer vision in various fields such as autonomous navigation, robotics, agriculture, and environmental monitoring. Accurate identification of different terrain types helps intelligent systems make better decisions in realworld environments. This project presents the development of a Computer Visionbased Terrain Analysis System using Deep Learning techniques. The proposed system utilizes a Convolutional Neural Network (CNN) to classify terrain images into four categories: Grass Terrain, Marshy Terrain, Rocky Terrain, and Sandy Terrain. Image preprocessing techniques such as resizing and normalization are applied to improve classification performance. The CNN model is developed using TensorFlow and Keras, while OpenCV is used for image processing. A Flask-based web application provides a simple interface for uploading terrain images and displaying prediction results. The proposed system improves classification accuracy by automatically extracting important visual features without manual intervention. It provides an efficient, reliable, and user-friendly solution for automated terrain recognition and can be applied in autonomous vehicles, precision agriculture, robotic navigation, and environmental monitoring.
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