Enhanced Infrared Defect Detection for UAVs Using Wavelet-Based Image Processing and Channel Attention-Integrated SSD Model Article Swipe
Jining Zhao
,
R. Zhang
,
Shengyong Chen
,
Yihui Duan
,
Zhiyuan Wang
,
Qingchen Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3516080
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3516080
In this paper, we develop a defect target detection algorithm based on image processing and feature matching to address background noise in the detection of defects in infrared images of Unmanned Aerial Vehicle (UAVs), as well as to improve real-time monitoring capabilities. We begin by constructing an infrared defect image processing model using wavelet multilayer decomposition, which effectively suppresses texture information within the complex background by reconstructing low-frequency and high-frequency coefficient images. To further enhance defect feature extraction, we segment and filter the reconstructed infrared image to obtain a clearer representation of infrared defect features. We also design a feature point matching algorithm that integrates SURF (Speeded-Up Robust Features) for feature extraction and target image matching. Finally, to further improve the real-time performance and accuracy of defect detection, we enhance the SSD (Single Shot MultiBox Detector) target detection algorithm by introducing the Channel Attention Mechanism (CAM) to create the SSD-CA model. Experimental results demonstrate that the proposed defect detection model achieves a defect detection accuracy of 99.4% on both the IT-UAV and MVTecAD datasets, with a recall rate of 97.68% after 3,000 iterations. Moreover, the results indicate that the network incorporating the channel attention mechanism significantly improves detection performance compared to the network without it, with the defect detection index improving by 1.11%, and the false detection and leakage rates reduced by 0.69% and 0.44%, respectively. These findings suggest that the SSD-CA model proposed in this paper not only effectively extracts and enhances defect features but also intelligently suppresses non-essentia feature information in the background, thereby enabling real-time automatic detection of infrared image defects. This provides robust technical support for UAV inspection and maintenance in complex environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3516080
- OA Status
- gold
- Cited By
- 3
- References
- 27
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4405304009Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3516080Digital Object Identifier
- Title
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Enhanced Infrared Defect Detection for UAVs Using Wavelet-Based Image Processing and Channel Attention-Integrated SSD ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Jining Zhao, R. Zhang, Shengyong Chen, Yihui Duan, Zhiyuan Wang, Qingchen LiList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3516080Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2024.3516080Direct OA link when available
- Concepts
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Computer science, Wavelet, Artificial intelligence, Computer vision, Wavelet transform, Image processing, Channel (broadcasting), Image (mathematics), Pattern recognition (psychology), TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4395669328, https://openalex.org/W3202659840, https://openalex.org/W4392309467, https://openalex.org/W2972158684, https://openalex.org/W4402772491, https://openalex.org/W4399711193, https://openalex.org/W4391260595, https://openalex.org/W4400700394, https://openalex.org/W4398240529, https://openalex.org/W4387779880, https://openalex.org/W4399114166, https://openalex.org/W4221057665, https://openalex.org/W4391692106, https://openalex.org/W4394805406, https://openalex.org/W4393142641, https://openalex.org/W4387125908, https://openalex.org/W4388566466, https://openalex.org/W4390057581, https://openalex.org/W4391167220, https://openalex.org/W4396753607, https://openalex.org/W2334445587, https://openalex.org/W4313252134, https://openalex.org/W3041134889, https://openalex.org/W4389053153, https://openalex.org/W4390678457, https://openalex.org/W4391307050, https://openalex.org/W4392897397 |
| referenced_works_count | 27 |
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| abstract_inverted_index.Finally, | 116 |
| abstract_inverted_index.MultiBox | 134 |
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| abstract_inverted_index.compared | 199 |
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| abstract_inverted_index.mechanism | 194 |
| abstract_inverted_index.real-time | 39, 121, 257 |
| abstract_inverted_index.technical | 267 |
| abstract_inverted_index.background | 19, 64 |
| abstract_inverted_index.detection, | 127 |
| abstract_inverted_index.extraction | 111 |
| abstract_inverted_index.inspection | 271 |
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| abstract_inverted_index.effectively | 57, 239 |
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| abstract_inverted_index.reconstructed | 83 |
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| abstract_inverted_index.reconstructing | 66 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
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| citation_normalized_percentile.is_in_top_10_percent | False |