IDD-YOLOv7: A lightweight and efficient feature extraction method for insulator defect detection
文献类型：期刊论文
| 作者 | Zhao, Yongxiang3,5; Zhang, Guoqing5; Luo, Wei1,2,4,5; Tang, Ruiyin5; Sun, Ying5; Wang, Penggang5; Liu, Jiandong5; Mei, Keyu5 |
| 刊名 | ENERGY REPORTS
 |
| 出版日期 | 2025-06-01 |
| 卷号 | 13页码:1467-1487 |
| 关键词 | Insulator defect detection Lightweight model Efficient feature extraction IDD-YOLOv7 |
| ISSN号 | 2352-4847 |
| DOI | 10.1016/j.egyr.2024.12.076 |
| 通讯作者 | Luo, Wei(luowei@radi.ac.cn) |
| 英文摘要 | Aiming at the challenges of diversity, large scale variation and complex background in insulator defect images captured by UAVs, as well as the insufficient ability of existing detection algorithms in terms of leakage and false detection of small-size defects and adaptation to complex backgrounds, this study proposes a lightweight and efficient feature extraction method for insulator defects detection based on YOLOv7, named IDD-YOLOv7. First, we propose a Multi-Scale Channel Information Extraction Module (MCIE), enabling the model to effectively learn and utilize feature map information at different scales to address the challenge of significant scale variations in defect areas. Second, we propose a Contextual Global-Local Attention Module (CG-LA), allowing the model to consider both global context information and local details to tackle background interference issues. Additionally, we design a Focused Pure Convolution Feature Extraction Module (FPCFE) to enhance the model's focus on tiny insulator defects, effectively addressing the issues of missed detections and false positives in existing algorithms for small-sized defects. To improve the model's robustness and generalization ability, we apply data augmentation to the defect dataset. Finally, we utilize channel pruning and knowledge distillation strategies to compress the improved model, making it lightweight enough for deployment on UAV platforms with limited computational resources. The experimental results show that compared with the baseline model, IDD-YOLOv7 improves the AP50 in breakage-defect, drop-defect and flashover-defect by 10.3 %, 8.8 % and 10.8 %, respectively, and the mAP improves by 8.9 %, and the physical storage space occupies only 5.6MB, which is compared to YOLOv7 reduced by 69 %. Compared with the existing detection algorithms, IDD-YOLOv7 is not only able to detect various insulator defects quickly and accurately, but also has low leakage and false detection rates. Taken together, the proposed algorithm has significant robustness in insulator defect detection. |
| WOS关键词 | MODEL |
| 资助项目 | Open Project Funds for the Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics) , Ministry of Industry and Information Technology[NJ2024027-8] ; Fundamental Research Funds for the Central Universities[NJ2024027] |
| WOS研究方向 | Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001403245500001 |
| 出版者 | ELSEVIER |
| 资助机构 | Open Project Funds for the Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics) , Ministry of Industry and Information Technology ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212894]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Luo, Wei |
| 作者单位 | 1.Aerosp Remote Sensing Informat Proc & Applicat Col, Langfang 065000, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Space Engn Univ, Sch Space Informat, Beijing 101407, Peoples R China 4.Nanjing Univ Aeronaut & Astronaut, Minist Ind & Informat Technol, Key Lab Space Photoelect Detect & Percept, Nanjing 211100, Jiangsu, Peoples R China 5.North China Inst Aerosp Engn, Langfang 065000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Yongxiang,Zhang, Guoqing,Luo, Wei,et al. IDD-YOLOv7: A lightweight and efficient feature extraction method for insulator defect detection[J]. ENERGY REPORTS,2025,13:1467-1487. |
| APA | Zhao, Yongxiang.,Zhang, Guoqing.,Luo, Wei.,Tang, Ruiyin.,Sun, Ying.,...&Mei, Keyu.(2025).IDD-YOLOv7: A lightweight and efficient feature extraction method for insulator defect detection.ENERGY REPORTS,13,1467-1487. |
| MLA | Zhao, Yongxiang,et al."IDD-YOLOv7: A lightweight and efficient feature extraction method for insulator defect detection".ENERGY REPORTS 13(2025):1467-1487. |
入库方式： OAI收割
来源：地理科学与资源研究所
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