ENHANCED TERRAIN RECOGNITION WITH DEEP LEARNING

Abstract

Terrain recognition is vital for numerous real-world applications, from autonomous navigation to disaster management. Convolutional Neural Networks (CNNs) have emerged as potent tools for addressing terrain recognition challenges. In this paper, we propose an innovative approach to significantly improve terrain recognition accuracy using CNNs. We meticulously curate a dataset from Kaggle, comprising 1989 high-resolution images categorized into four terrain classes: Grassy, Sandy, Rocky, and Marshy. Our methodology revolves around the systematic design and implementation of deep learning techniques, primarily focusing on CNN architectures. Additionally, we contribute by training a CNN model tailored for classifying images into the four terrain classes. Leveraging the computational resources of Google Colab, we conduct extensive experimentation and analysis to evaluate the performance of our CNN-based terrain recognition system. Empirical results demonstrate substantial advancements in terrain recognition accuracy, underscoring the transformative role of CNNs in enhancing the efficiency and precision of terrain classification systems. Furthermore, we delve into the intricacies of our CNN model's architecture, exploring key design choices and optimization strategies. These insights deepen our understanding of CNN- based terrain recognition systems and provide valuable guidance for future research endeavors. Overall, our study highlights the practical relevance and transformative potential of CNNs in elevating terrain recognition accuracy. Keywords: Terrain recognition, Convolutional Neural Networks (CNNs), Image classification, Real-world applications, Dataset, Performance evaluation, Optimization strategies, Deep learning