YOLOv7-based insulator defect detection with progressive feature fusion and DFC attention
Year: 2024
Type: conference-paper
Abstract: With the development of smart grids, drones have been widely used for inspecting power transmission lines, generating a large amount of insulator image data. Relying on manual inspection for this task results in a huge workload and the risk of missed detections due to human fatigue. Here, we propose an improved model based on YOLOv7-Tiny. First, we replace the activation function with the funnel activation function to optimize the activation domain dynamically. Second, we introduce a lightweight DFC attention mechanism. Experimental results show that the improved model achieves a detection accuracy and recall rate of 96.4% and 92.5%, respectively.
Authors: Xilai You, Jianqiao Ma
Institution: Lanzhou Jiaotong University
Language: English
Topic: Power Line Inspection Robots
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404068631