This blog post details how to use deep learning to build an advanced transmission line equipment inspection system.
The system also integrates a user-friendly interface based on PySide6, which makes operation more convenient. The detection system can efficiently and accurately identify and classify various devices on the transmission line, and supports detection through a variety of methods such as pictures, picture folders, video files, and real-time monitoring of cameras. A major highlight of the system is that it includes a variety of data analytics functions, such as histogram analysis, marker box categories, category statistics, and allows users to adjust the confidence level threshold (Conf) and crossover ratio (IOU) parameters as needed to optimize the detection effect and visualize the results.
In addition, the system also designs a user management interface based on SQLite database, which supports users to switch models and customize UI to meet the specific needs of different users.
Due to the influence of natural environment and human factors, transmission line equipment often faces the risk of damage or aging, which not only affects the stability of power supply, but also may cause safety accidents, causing serious economic losses and social impact. Therefore, regular inspection and maintenance of transmission lines and their equipment is particularly important. Traditional transmission line equipment inspection relies on manual inspection, which is not only inefficient, costly, but also has great security risks. With the rapid development of computer vision and deep learning technology, automatic detection technology based on image processing has become a research hotspot, providing new solutions for transmission line equipment inspection.
The introduced dataset contains a total of 10,590 high-resolution images, which are carefully divided into a training dataset, a validation set, and a test set. Specifically, the training dataset contains 9,256 images, which are the main source of model learning; the validation set contains 874 images, which are used for model tuning and validation; and the test set contains 460 images, which are used as the final challenge to evaluate the generalization ability of the model.
In these images, we annotate a variety of transmission line equipment, including transmission lines, dampers, insulators, etc. Each category is equipped with precise bounding boxes and category labels. In order to enhance the recognition ability of the model, especially in complex environments, we perform a series of preprocessing and data enhancement processing on the image. This includes operations such as standardization of image size, color normalization, and random transformation, such as rotation, flipping, scaling, and cropping. These steps are not only able to simulate various changes in the real world, but also help to avoid model overfitting and enhance its generalization ability.
On the main interface, the system provides functions to support the input of pictures, videos, real-time cameras and batch files. Users can select the pictures or videos to be inspected for transmission line equipment by clicking the corresponding buttons, or start the camera for real-time detection. During detection, the system will display the detection results in real time and store the detection records in the database.
The system also provides the function of one-click replacement of YOLOv8 models. Users can select a different YOLOv8 model for detection by clicking the “Change Model” button on the interface. At the same time, the dataset attached to the system can also be used to retrain the model to meet the user’s detection needs in different scenarios.