TinyML pipeline for efficient crack classification in UAV-based structural health inspections
Yuxuan Zhang, Arne Nürnberg, Luciano Sebastian Martinez Rau, Quynh Nguyen Phuong Vu, Yuchen Lu, Bengt Oelmann, Sebastian Bader
Abstract
Structural health monitoring (SHM) of civil, aerospace, and energy infrastructure increasingly relies on UAVs with vision sensors for efficient inspections. Crack classification is a central task, yet cloud-based inference introduces bandwidth, power, connectivity, and privacy challenges that limit its practicality. This study presents a fully self-contained Tiny Machine Learning (TinyML) pipeline for onboard crack classification on a milliwatt-level STM32H7 microcontroller. Using MobileNetV1x0.25 as the baseline, we systematically evaluate the full measurement pipeline, including image capture, preprocessing, and inference on a low-power embedded system. Two preprocessing strategies, a handcrafted sequence (grayscale, contrast, denoise, median, binarization) and a greedy algorithm-based composite method, are compared. Four compression techniques, namely post-training quantization (PTQ), quantization-aware training (QAT), pruning, and weight clustering, are assessed individually and in combination. The optimized pipeline achieves an F1-score of 0.938, an improvement of 11.4% over state-of-the-art deployments. At the same time, it requires only 2.9 MB RAM and 309 KB flash, with an end-to-end latency of 461.6 ms and an energy cost of 623.16 mJ per inference. On a DJI Mini 4 Pro UAV, continuous operation reduces flight time by just 1.31 minutes (4%), compared to 8 minutes (24%) when using Jetson-based platforms. Overall, this work delivers a reproducible benchmark for UAV-based SHM, demonstrating a practical balance of accuracy, resource efficiency, and energy consumption, and advancing the feasibility of on-device crack classification in highly resource-constrained environments.