"基于改进YOLOv3的绝缘子串定位与状态识别方法"

Insulator String Localization and State Recognition Method based on Improved YOLOv3 Algorithm" is a Bachelor's degree thesis from Southwestern University of Finance and Economics. The thesis focuses on developing a method to localize insulator strings and recognize their states using the YOLOv3 convolutional neural network. The YOLOv3 algorithm is a popular deep learning model for object detection and recognition tasks. By improving upon this model, the thesis aims to provide a more accurate and efficient method for locating insulator strings in images and classifying their states. The thesis begins with an introduction to the problem of insulator string localization and state recognition, highlighting the importance of this task in the field of electrical engineering. It then reviews related work in the area of object detection and recognition using convolutional neural networks. The methodology section of the thesis details the implementation of the improved YOLOv3 algorithm for insulator string localization and state recognition. This includes data preprocessing techniques, model training procedures, and evaluation methods for assessing the performance of the algorithm. The results section presents the experimental results of the proposed method, demonstrating its effectiveness in accurately localizing insulator strings and recognizing their states. The thesis concludes with a discussion of the implications of this research and suggestions for future work in this area. In summary, the "Insulator String Localization and State Recognition Method based on Improved YOLOv3 Algorithm" thesis provides a novel approach to solving the problem of insulator string localization and state recognition using a state-of-the-art deep learning model. The research contributes valuable insights to the field of electrical engineering and offers a promising method for improving the efficiency and accuracy of insulator string inspection in practical applications.