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Concrete crack detection is essential for infrastructure safety, and its detection efficiency and accuracy are the key issues. An improved YOLOV5 and three measurement algorithms are proposed in this paper, where the original prediction heads are replaced by Transformer Heads (TH) to expose the prediction potential with one self-attention model. Experiments show that the improved YOLOV5 effectively enhances the detection and classification of concrete cracks, and the Mean Average Precision (MAP) value of all classes increases to 99.5%. The first method is more accurate for small cracks, whilst the average width obtained based on the axial traverse correction method is more exact for large cracks. The crack width obtained from the concrete picture sample is the same as that obtained from the manual detection, with a deviation rate of 0–5.5%. This research demonstrates the recognition and classification of concrete cracks by integrating deep learning and machine vision with high precision and high efficiency. It is helpful for the real-time measurement and analysis of concrete cracks with potential safety hazards in bridges, high-rise buildings, etc. [ABSTRACT FROM AUTHOR]

Remotely operated vehicles (ROVs) and unmanned aerial vehicles (UAVs) provide a solution for dam and bridges structural health information acquisition, but problems like effective damage-related information extraction also occur. Vision-based crack detection methods can replace traditional manual inspection and achieve fast and accurate crack detection. This paper thereby proposes a lightweight, real-time, pixel-level crack detection method based on an improved instance segmentation model. A lightweight backbone and a novel efficient prototype mask branch are proposed to decrease the complexity of the model and maintain the accuracy of the model. The proposed method attains an accuracy of 0.945 at 129 frames per second (FPS). Moreover, our model has smaller volume, lower computational requirements and is suitable for low-performance devices. [ABSTRACT FROM AUTHOR]

Background: The interaction of cell and medical biomaterial is one of the significant factors to affect clinical application of medical biomaterial. This research is to investigate three of suture lines how to affect the proliferation and cell cycle of Lung Adenocarcinoma cell A549., Methods: Three of suture lines are respectively cultivated with Lung Adenocarcinoma cell A549, after of 72 hours, to detect absorptance of each groups by MTT method in order to reflect the proliferation of Lung Adenocarcinoma cell A549 and to detect percentage of G1 period cell and S period cell of each of groups by flow cytometry., Results: Different of suture lines have effect differently on the proliferation and cell cycle of Lung Adenocarcinoma cell A549 (P<0.05). The effect of absorbent suture line on the proliferation and cell cycle of Lung Adenocarcinoma cell A549 is strong, the effect of chorda serica chirurgicalis is medium, the effect of slide wire is poor. Different length of each suture line have effect differently on the proliferation and cell cycle of Lung Adenocarcinoma cell A549 (P<0.05)., Conclusions: Three of suture line materials have different effect on the proliferation and cell cycle of Lung Adenocarcinoma cell A549, to reflect dose-effect relation.