AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION
This project aims to construct an autonomous power line inspection system using computer vision to classify and localise the normal and abnormal insulators. The traditional inspection methods, for instance, foot patrol inspection and helicopter-assisted inspection are time-consuming and dangerous. D...
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| 格式: | Final Year Project Report |
| 语言: | English English |
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Universiti Malaysia Sarawak, (UNIMAS)
2022
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| 在线阅读: | http://ir.unimas.my/id/eprint/40114/1/Law%20Jin%20Ming%2024pgs.pdf http://ir.unimas.my/id/eprint/40114/5/Law%20Jin%20Ming%20ft.pdf http://ir.unimas.my/id/eprint/40114/ |
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my.unimas.ir.401142024-01-10T06:58:24Z http://ir.unimas.my/id/eprint/40114/ AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION LAW, JIN MING TA Engineering (General). Civil engineering (General) This project aims to construct an autonomous power line inspection system using computer vision to classify and localise the normal and abnormal insulators. The traditional inspection methods, for instance, foot patrol inspection and helicopter-assisted inspection are time-consuming and dangerous. DenseNet-201 model is proposed as the base network to perform insulator fault detection autonomously. An algorithm with DenseNet-201 backbone consisting of two branches which are class label classification and bounding box regression is developed. The developed algorithm is trained on the augmented Chinese Power Line Insulator Dataset (CPLID) that consisted of normal and missing cap insulator images. The prediction results in classification accuracy of 100%. The average precision of detecting normal insulator and insulator missing cap has higher performance at threshold 0.3 which are 100% and 66.61%. The mean average precision at thresholds 0.3 and 0.5 are 83.31% and 64.62% respectively. The experimental results on the augmented CPLID dataset denote that the proposed model has high classification accuracy and it outperforms the ResNet model. Universiti Malaysia Sarawak, (UNIMAS) 2022 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/40114/1/Law%20Jin%20Ming%2024pgs.pdf text en http://ir.unimas.my/id/eprint/40114/5/Law%20Jin%20Ming%20ft.pdf LAW, JIN MING (2022) AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION. [Final Year Project Report] (Unpublished) |
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| collection |
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| content_source |
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| language |
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| topic |
TA Engineering (General). Civil engineering (General) |
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TA Engineering (General). Civil engineering (General) LAW, JIN MING AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION |
| description |
This project aims to construct an autonomous power line inspection system using computer vision to classify and localise the normal and abnormal insulators. The traditional inspection methods, for instance, foot patrol inspection and helicopter-assisted inspection are time-consuming and dangerous. DenseNet-201 model is proposed as the base network to perform insulator fault detection autonomously. An algorithm with DenseNet-201 backbone consisting of two branches which are class label classification and bounding box regression is developed. The developed algorithm is trained on the augmented Chinese Power Line Insulator Dataset (CPLID) that consisted of normal and missing cap insulator images. The prediction results in classification accuracy of 100%. The average precision of detecting normal insulator and insulator missing cap has higher performance at threshold 0.3 which are 100% and 66.61%. The mean average precision at thresholds 0.3 and 0.5 are 83.31% and 64.62% respectively. The experimental results on the augmented CPLID dataset denote that the proposed model has high classification accuracy and it outperforms the ResNet model. |
| format |
Final Year Project Report |
| author |
LAW, JIN MING |
| author_facet |
LAW, JIN MING |
| author_sort |
LAW, JIN MING |
| title |
AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION |
| title_short |
AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION |
| title_full |
AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION |
| title_fullStr |
AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION |
| title_full_unstemmed |
AUTONOMOUS POWER LINE INSPECTION USING COMPUTER VISION |
| title_sort |
autonomous power line inspection using computer vision |
| publisher |
Universiti Malaysia Sarawak, (UNIMAS) |
| publishDate |
2022 |
| url |
http://ir.unimas.my/id/eprint/40114/1/Law%20Jin%20Ming%2024pgs.pdf http://ir.unimas.my/id/eprint/40114/5/Law%20Jin%20Ming%20ft.pdf http://ir.unimas.my/id/eprint/40114/ |
| _version_ |
1789430346069049344 |
| score |
13.251813 |