Application of Mathematical Statistics Method Based on YOLOv3 Algorithm in Measurement and Detection Data

With the development of Internet visual algorithms, the target demand of measurement and detection data analysis is optimized constantly. In the view of wide application of target detection technology in various industries, mathematical statistics based on YOLOv3 (You Only Look Once) algorithm is increasingly important for the analysis of network detection data in different industries. In the construction of the entire target metric detection model, how to enhance accuracy of data detection and decrease model data processing time is currently significant issue that requires to be instantly resolved. This article studied the optimization model of target detection using the YOLOv3 algorithm and the analysis of metric detection, aiming to enhance the accuracy and efficiency of model detection. In this paper, through the simulation experiment analysis of COCO (Common Objects in Context) dataset, the mathematical statistics method based on YOLOv3 algorithm was applied to research, and the flow chart design was optimized by combining multi-scale improvement and loss function. Based on the discussion of data results, the following conclusion can be drawn. In the context of internet big data, optimization model for object detection using YOLOv3 algorithm has improved detection accuracy in the four sampled test domain parameters, and overall average improvement is 6%. During this time, there have been improvements in detection efficiency, and the overall average improvement is 10.25%. This shows that the mathematical statistics method based on YOLOv3 algorithm has a good practical application effect in the measurement and detection data analysis. This method combines deep learning technology and mathematical statistical methods to make the analysis of metrological detection data more robust, and can cope with complex environmental conditions and diverse measurement targets. It is expected to play a key role in fields such as industrial quality inspection, traffic monitoring, and medical image analysis.