FocusNet: A Lightweight Insulator Defect Detection Network via First-Order Taylor Importance Assessment and Knowledge Distillation
Year: 2025
Type: article
Abstract: In the detection of small targets such as insulator defects and flashovers, the existing YOLOv11 has problems such as insufficient feature extraction and difficulty in balancing model lightweight and detection accuracy. We propose a lightweight architecture called FocusNet based on YOLOv11n. To improve the feature expression ability of small targets, Aggregation Diffusion Neck is designed to achieve deep integration and optimization of features at different levels through multiple rounds of multi-scale feature fusion and scale adaptation, and Focus module is introduced to focus on and strengthen the key features of small targets. On this basis, to achieve efficient deployment, the Group-Level First-Order Taylor Expansion Importance Assessment Method is proposed to eliminate channels that have little impact on detection accuracy to streamline the model structure. Then, Channel Distribution Distillation compensates for the slight accuracy loss caused by pruning, and finally achieves the dual optimization of high accuracy and high efficiency. Furthermore, we analyze the interpretability of FocusNet via heatmaps generated by KPCA-CAM. Experiments show that FocusNet achieves 98.50% precision and 99.20% [email protected] on a proprietary insulator defect detection database created for this project using only 3.80 GFLOPs. This research provides reliable technical support for insulator monitoring in power systems.
Source: Algorithms
DOI: 10.3390/a18100649
Authors: Yurong Jing, Zhiyong Tao, Sen Lin
Institutions: Liaoning Technical University, Shenyang Ligong University
Language: English
Topic: Power Line Inspection Robots
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4415239114