A insulator defect detection network based on improved YOLOv7 for UAV aerial images
Year: 2025
Type: article
Abstract: The failure of insulators can compromise the safety of the entire power transmission system. Therefore, insulator identification and defect detection from massive UAV aerial image data has become an urgent problem to be solved in the field of operation and maintenance . To evaluate insulator integrity, we propose a lightweight insulator defect detection network model based on improved Yolo v7. First of all, it is proposed to use the long side definition method to add angle information to the detection frame, preprocess the aerial image of the UAV , and realize the target rotation frame detection, which helps to accurately identify and locate the insulator. Secondly, combining the deformable convolution with the ELAN module as the backbone network , a DCN-ELAN module is proposed to enable the model to learn more defect shapes, and a multi-scale bidirectional feature fusion module (MBFF) is added to adaptively adjust the weight of each feature map to enhance the adaptability to various targets. Especially improve the detection ability of small targets. Finally, RefConv convolution is used as the core to build a lightweight information representation network. This technique improves the network’s capability to handle image boundary details, simplifies the model’s complexity, and reduces computational requirements. The generalization of the model is verified on VOC and IDID datasets, and the experimental results show that the proposed method achieves better insulator defect detection performance than other methods.
Source: Measurement
Authors: Xingyi You, Xiaohu Zhao
Institution: China University of Mining and Technology
Language: English
Topic: Power Line Inspection Robots
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,760
Award: G4360092042
OpenAlex ID: W4409209908