Defect Detection from UAV Images Based on Region-Based CNNs

Liu, Meng; Zhang, Yipeng; Zhang, Lefei; Du, Bo

doi:10.1109/icdmw.2018.00063

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…This study built an imbalanced dataset for defect detection so as to mimic the real environment task of finding new defects from many non‐defect cases (Lan et al., 2018). To create the dataset for defect detection, this study selected 50 random defect images from the developed defect classification dataset and 1600 random non‐defect images from Peer Hub ImageNet (Φ‐Net) Task 2 (Y. Gao & Mosalam, 2020) and shuffled them.…”

Section: Experiments Designmentioning

confidence: 99%

Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model

Yong

Jeon

Gil

et al. 2022

Computer aided Civil Eng

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Zero‐shot learning, applied with vision‐language pretrained (VLP) models, is expected to be an alternative to existing deep learning models for defect detection, under insufficient dataset. However, VLP models, including contrastive language‐image pretraining (CLIP), showed fluctuated performance on prompts (inputs), resulting in research on prompt engineering—optimization of prompts for improving performance. Therefore, this study aims to identify the features of a prompt that can yield the best performance in classifying and detecting building defects using the zero‐shot and few‐shot capabilities of CLIP. The results reveal the following: (1) domain‐specific definitions are better than general definitions and images; (2) a complete sentence is better than a set of core terms; and (3) multimodal information is better than single‐modal information. The resulting detection performance using the proposed prompting method outperformed that of existing supervised models.

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Section: Experiments Designmentioning

confidence: 99%

Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model

Yong

Jeon

Gil

et al. 2022

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…And an envelope-based piecewise fitting method to fit the power line. With the rapid development of deep learning and the disadvantages of versatility and stability of traditional image processing methods [15][16][17]. Deep learning methods are gradually being used to detect power line [2,18,19].…”

Section: Research Vision-based Uav Distribution Line Inspection Using Deep Learningmentioning

confidence: 99%

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Chen

Zhuang

Wang

et al. 2022

Preprint

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Regular inspection of distribution line is an important link to maintain the normal operation of distribution network. Using unmanned aerial vehicle (UAV) instead of manpower can save the cost of inspection. With the universal application of vision sensor in UAV and the rapid development of deep learning, Convolutional Neural Networks (CNN) is applied to the detection of power line in UAV visible images. In view of the lack of application environment inspection methods for distribution line, a vision-based UAV distribution line inspection method using deep learning and a dataset for the deep learning method of distribution line inspection task are proposed in this paper. The method proposed predicts distribution line area through the encoder-decoder structure network firstly. Image processing operation and sampling clustering are used to remove the interference. Finally, the UAV tracking direction of distribution power line is calculated according to the detected distribution line. The method can reach an inspection speed of nearly 77ms per frame, the range of heading deviation error can reach (-1.52°, 1.36°), the tracking rate nearly 100%. Through the test of network and inspection method on dataset, the results show that the method proposed in this paper can be effectively, quickly and accurately applied to UAV distribution line inspection.

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“…There are many types of components including tower, conductor and accessories (e.g., insulator and fitting) attached to them, and each component type has various faults. In this paper, we summarize the power line components into four categories including insulator, tower, conductor and fitting [27].…”

Section: Power Line Components and Their Common Faultsmentioning

confidence: 99%

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

Liu,

Miao,

Jiang

et al. 2020

Preprint

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The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial works have been conducted on the analysis of power lines inspection data. With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research. Following the typical procedure of inspection data analysis, we categorize current works in this area into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Some valuable information is also included such as data description and method performance. Further, an in-depth discussion of existing deep-learning-related analysis methods in power lines inspection is proposed. Finally, we conclude the paper with several research trends for the future of this area, such as data quality problems, small object detection, embedded application, and evaluation baseline.

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Defect Detection from UAV Images Based on Region-Based CNNs

Cited by 24 publications

References 27 publications

Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model

Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

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