Accurate deflection measurement is vital in evaluating the structural integrity of transportation infrastructures, with bridges being fascinating. In this study, we propose a novel image stabilization technique integrated into the sampling moiré method, which leads to a dependable approach for measuring bridge deflection through drone aerial photography. Our experimental verification entailed conducting drone tests on an actual bridge, utilizing a passing test vehicle, and the results showcased deflection measurements comparable to those obtained through conventional methods. This newly developed technology eliminates the need for ground-fixed cameras mounted on tripods, thus enabling precise deflection measurement at the millimeter level for bridges in challenging environments, including marine and mountainous areas.
Social infrastructures are rapidly aging, and there are concerns about the increasing cost and effort required for maintenance and management. Deflection measurement is critical in evaluating the integrity of bridges as transportation infrastructure. The sampling moiré method was developed to accurately measure the displacement of structures by capturing the regular patterns (i.e., moiré markers) attached to the structures with a digital camera. The conventional approach rigidly attaches a camera to a tripod or a fixed point. However, finding a place to photograph bridges over the sea or mountains can be challenging in real scenarios. In recent years, camera-equipped drones have rapidly become an inspection technology for bridges and other transportation infrastructure. Here, we believe drone cameras open a new door for unconstrained bridge vision inspection using the moiré phase analysis method. Therefore, we are striving to develop a novel displacement measurement method that can measure the deflection of bridges by drone aerial photography. We measured the vertical displacement of a target with a 50-mm-pitch grid pattern in the laboratory to verify its effectiveness. The newly developed measurement technology alleviates the restriction that the camera must be fixed and enables the measurement of the deflection of a more significant number of bridges under various real situations.
Computer-aided diagnosis (CAD) has gained considerable attention for breast cancer screening owing to its high diagnostic efficiency and satisfactory accuracy. However, it has been revealed that traditional CAD systems for mammography are vulnerable to dense breast tissue, which could hide underlying tumors. To resolve this issue, we devised a learning scheme that equips the U-Net backbone with a well-designed attention mechanism to suppress the over-detection rate for nongland mammary regions in dense breast tissue and applied to the CAD for breast ultrasound (BUS) images. The proposed method has two stages: initial mammary gland segmentation, which involves the selection of a region in the mammary gland where a tumor may occur; then tumor region segmentation, wherein the attention U-Net detects tumor regions by characterizing the selected mammary gland probability map as a spatial attention map, drawing selective attention to mammary gland tissues. We evaluated the proposed tumor detection scheme on several public BUS image datasets. Comparative results demonstrate that the proposed approach achieves the best performance in most conditions. Notably, when considering the percentage of all actual tumors that were correctly segmented, the proposed method showed a tumorwise accuracy performance of 92.7%.
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