A Low-Cost UAV-Based Framework for Post-Seismic Crack Detection with CNN and Gesture Control

Traditional post-earthquake inspections are slow, costly, and subject to human error. This paper presents an autonomous structural inspection system that combines a low-cost unmanned aerial vehicle (UAV) with a Proportional-Derivative (PD) flight controller, a convolutional neural network (CNN) for crack detection, and a gesture-based user interface for intuitive operation. Implemented on a DJI Tello platform, the system achieves 98% validation accuracy on a dataset of 15,594 images while maintaining stable flight and executing predefined inspection trajectories via hand gestures. Results indicate the feasibility of integrating UAVs and deep learning to optimize post-seismic inspection workflows.