The Method of Anomaly Location Data Recognition Based on Improved YOLO Algorithm

The existing anomaly location data recognition methods usually have poor accuracy due to the rough contour curve, so the anomaly location data recognition method is studied based on the improved YOLO algorithm. The improved YOLO algorithm is designed to judge the input and output residual error comparison in the normalization process. Based on the algorithm, the abnormal data location technology is studied, and the contour curve with low noise factor is obtained. Based on the improved YOLO algorithm, the abnormal location data recognition method is designed, and the accuracy of the method is optimized The experimental results show that in the calculation of the first type error rate and the second type error rate, the slope of the method is gentle, and the value is small. It can be seen that the method will not produce large numerical changes under the changes of mathematical expectation and regression parameters, and can more accurately realize the anomaly location data recognition.

Automated summary

In this study, an anomaly location data recognition method based on the improved YOLO algorithm is proposed to address the issue of poor accuracy in existing methods. By optimizing the algorithm design, a more accurate and noise-insensitive contour curve is obtained, and experimental results demonstrate the effectiveness and stability of the method.