دورية أكاديمية
An improved cascade RCNN detection method for key components and defects of transmission lines
| العنوان: | An improved cascade RCNN detection method for key components and defects of transmission lines |
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| المؤلفون: | Chao Dong, Ke Zhang, Zhiyuan Xie, Chaojun Shi |
| المصدر: | IET Generation, Transmission & Distribution, Vol 17, Iss 19, Pp 4277-4292 (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations |
| مصطلحات موضوعية: | artificial intelligence, computer vision, fault diagnosis, image processing, power transmission faults, power transmission lines, Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841 |
| الوصف: | Abstract Overhead transmission line detection based on deep learning of aerial images taken by UAVs has been widely investigated. Despite its success, it is limited by several factors, including inappropriate evaluation criteria and dramatic scaling of components in the images. To mitigate these issues, a relative mean Average Precision evaluation index is proposed to accurately measure the model's detection performance for smaller objects. A data enhancement strategy including multi‐scale transformation is adopted to alleviate the problem of drastic scaling. The existing Cascade RCNN target detection technology is enhanced by incorporating Swin‐v2 and a balanced feature pyramid to improve feature characterization capabilities, while side‐aware boundary localization is utilized to improve the positioning accuracy of the model. Experimental results demonstrate that the proposed method outperforms state‐of‐the‐art methods on CPLID and achieves 7.8%, 11.8%, and 5.5% higher detection accuracy than the baseline for mAP50, relative small and medium mAP, respectively. Additionally, the paper discusses the influence of adopted data enhancement on the robustness of the model. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-8695 1751-8687 |
| Relation: | https://doaj.org/toc/1751-8687; https://doaj.org/toc/1751-8695 |
| DOI: | 10.1049/gtd2.12948 |
| URL الوصول: | https://doaj.org/article/5dffa9ffd718498a9de62293ebf9b5ab |
| رقم الأكسشن: | edsdoj.5dffa9ffd718498a9de62293ebf9b5ab |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17518695 17518687 |
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| DOI: | 10.1049/gtd2.12948 |