Abstract
Reliable cement crack inspection is essential for structural health monitoring, particularly under challenging illumination conditions such as nighttime and low-light environments. Existing detection-based methods often require extensive bounding-box annotations, high computational resources, and show limited robustness in poor visibility conditions. This paper presents a lightweight RGB-thermal framework that integrates a MobileNetV4 classifier with a Convolutional Block Attention Module (CBAM) and an Enhanced Scale-Invariant Feature Transform (SIFT) localization algorithm for day–night crack inspection. Unlike conventional object detection approaches, the proposed method enables crack classification and localization without manual bounding-box annotation. The framework is evaluated using RGB and thermal images from three datasets, including the GYU-DET benchmark and two newly developed thermal crack datasets. On GYU-DET, the proposed classifier achieves a weighted F1-score of 83.16% using only region-of-interest-level labels, in contrast to the bounding-box-supervised YOLOv11n baseline on the same benchmark (mAP@0.5 = 0.568). Because the two approaches solve different sub-problems–joint detection-and-localization versus region-level classification–their results are reported as a capability comparison rather than a numerical improvement. Notably, crack, one of the two most difficult categories for the bounding-box detector, becomes one of the most reliably recognized categories once localization ambiguity is removed by the proposed decoupled design. For thermal morphology recognition, the original ROI-level evaluation yields weighted F1-scores of 92.25% and 92.69% on Datasets 2 and 3, respectively, within the collected crack set. A five-run crack-disjoint evaluation on Dataset 2, restricted to six morphology classes with sufficient numbers of independent physical cracks, yields an accuracy of 82.76±3.43% and a macro-F1 of 82.03±3.60%. End-to-end localization and classification from raw thermograms yields micro-F1 scores of 78.62% and 75.24% on Datasets 2 and 3, respectively. The Enhanced SIFT localization method, which requires no training, achieves F1-scores of 0.888 and 0.867 under passive and active thermal conditions, respectively, while operating within a 200 MB memory footprint on an entry-level 4 GB GPU. The lightweight and illumination-robust design enables reliable nighttime crack inspection and supports UAV-assisted and edge-based structural health monitoring applications.