Deep Visual Representation Structuring for Terrain Semantics via Efficient Feature Abstraction Mechanisms

Abstract

Autonomous outdoor robots are being widely adopted in application areas such as agriculture, environmental monitoring, disaster response, surveillance, and smart city mobility. Their effectiveness in operating within dynamic environments is highly dependent on reliable terrain understanding, as outdoor surfaces vary significantly in texture, composition, and stability. Traditional navigation methods that depend on sensors like ultrasonic, infrared, or LiDAR often face difficulties in interpreting visually complex or ambiguous terrains, which can lead to navigation errors and reduced efficiency. In addition, manual or sensor-driven terrain identification techniques are generally time-intensive, susceptible to inaccuracies, limited in scalability, and not well-suited for real-time large-scale data processing. To overcome these challenges, this work presents an automated vision-based terrain classification system that leverages computer vision and machine learning for improved robotic navigation. The framework employs MobileNetV2 as a feature extraction backbone to obtain rich visual representations from terrain images. These features are then processed using classification models such as Logistic Regression (LR), Naive Bayes Classifier (NBC), Ridge Classifier (RC), and eXtreme Gradient Boosting (XGBoost) to achieve accurate terrain categorization. The methodology includes image acquisition, preprocessing, deep feature extraction, and supervised learning for multi-class terrain recognition. By reducing reliance on conventional hardware sensors and manual analysis, the proposed system enhances accuracy, robustness, scalability, and cost efficiency. This vision-based approach ultimately supports safer navigation and improves the autonomy and performance of outdoor robotic systems operating in diverse real-world conditions.