With the advancement of technology and medicine, X-ray CT has been widely used in medical diagnosis, treatment, and monitoring of diseases. This article aims to further optimize the performance of X-ray fluorescence CT system by studying the relationship between contrast-to-noise ratio and the concentration and size of regions of interest (ROI). Using Geant4 XFCT simulation modeling, this study analyzes the impact of ROI concentration and size on the quality of XFCT reconstructed images. To assess the influence of different ROI concentrations on imaging performance of the X-ray fluorescence CT system, the simulation modeling system was adjusted for different ROI sizes, and twenty experimental groups were conducted. The results indicate that ROI concentration and size have a significant impact on imaging quality. Under specific conditions of concentration and size within the region of interest, optimal imaging effects of X-ray fluorescence CT can be achieved. These two factors interact, and when adjusting the parameters of ROI concentration and size to optimize imaging quality, it is necessary to consider the changes in both parameters rather than just the influence of a single parameter.
With the needs of urban infrastructure work, the number of sand trucks is increasing. Monitoring the illegal behavior of sand trucks is a highly repetitive and wasteful position of manpower and material resources. This paper presents an improved model based on YOLO V5s to identify the illegal behavior of sand trucks. This paper proposes the CA attention mechanism module to improve the network's attention to channel information, and then uses the ACON-C activation function to introduce two learnable dynamic parameters to the activation function to increase the nonlinearity of the network. Finally, the MPDIoU loss function is used. After the structure is improved, the same training parameters and data sets are used for different models through comparative experiments. The results show that the improved algorithm improves the mAP@0.5 index by 6.8%, the accuracy rate P and the recall rate R by 5.3% and 8.1%, respectively, compared with the original algorithm.
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