Adaptive Drone Landing Environment Recognition Using Deep Learning

Abstract

The Adaptive Drone Landing Environment Recognition Using Deep Learning framework is designed to improve the safety and reliability of autonomous drone landings by accurately recognizing suitable landing environments from aerial images. The proposed system employs deep learning and transfer learning techniques to classify different landing scenes captured by onboard cameras under diverse environmental conditions. Initially, aerial images are collected and preprocessed through resizing, normalization, and data augmentation to improve model performance and reduce overfitting. A pre-trained convolutional neural network is then fine-tuned to learn distinctive visual features associated with various landing environments, enabling efficient scene recognition even with limited training data. The trained model is evaluated using standard performance metrics to measure its classification capability and generalization performance. The proposed approach helps drones identify safe landing locations in both planned and emergency situations where predefined landing markers may not be available. By combining transfer learning with deep feature extraction, the framework improves recognition accuracy while reducing computational complexity and training time. Overall, the system provides an intelligent and practical solution for autonomous drone landing, supporting safer aerial navigation in real-world applications such as surveillance, disaster response, environmental monitoring, and precision agriculture.