UAV-based crack inspection of port quay walls is promising for efficient infrastructure maintenance, but its practical deployment remains hindered by frequent false positives caused by debris, stains, and irregular surface textures. This study proposes a false-positive reduction framework for a crack inspection system based on aerial images acquired by a small general-purpose UAV. The proposed method introduces anomaly detection after object detection so that detected crack candidate regions are re-evaluated based on their deviation from the learned feature distribution of crack images. A Vision Transformer (ViT)-based anomaly detection model is employed, and both standard-threshold and low-threshold object detection settings are investigated. Experimental validation across five verification areas showed that the combination of standard-threshold object detection and anomaly detection consistently improved F1 and F2 scores over the conventional baseline, demonstrating stable suppression of false positives while maintaining crack detectability. Under the low-threshold setting, Frangi filter-based preprocessing was more effective than grayscale-based preprocessing, achieving a favorable balance between broader crack extraction and false-positive suppression in some 5 m cases. However, this advantage decreased as image resolution deteriorated. Overall, the results indicate that the most robust configuration in the current framework is the combination of standard-threshold object detection and anomaly-based false-positive suppression. In contrast, the benefit of low-threshold operation depends strongly on image resolution. The findings also suggest that practical deployment requires calibration of the anomaly detection threshold based on site conditions and GSD.
Akage et al. (Thu,) studied this question.