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 au- tonomous 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.