A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images
Abstract Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. Current models are designed for sharp images, leading to potential detection failures in blurry ima...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01676-w |
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| author | Sayed Jobaer Xue-song Tang Yihong Zhang Gaojian Li Foysal Ahmed |
| author_facet | Sayed Jobaer Xue-song Tang Yihong Zhang Gaojian Li Foysal Ahmed |
| author_sort | Sayed Jobaer |
| collection | DOAJ |
| description | Abstract Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. Current models are designed for sharp images, leading to potential detection failures in blurry images. Using image deblurring before object detection is an option, but it demands significant computing power and relies heavily on the accuracy of the deblurring algorithms. Another common issue is the suitable dataset for the specific problem. To address the aforementioned issues, we develop a UAV-assisted small object detection dataset and propose a novel knowledge distillation method for object detection in blurry images in complex environments. Following this, we employ a technique known as self-supervised knowledge distillation, where we introduce a deblurring subnet module with the help of two attention modules, where both networks are trained in a fully-supervised manner. Based on the experiment results, our proposed model achieves an improvement of 4.3% accuracy in the VisDrone synthetic motion blur dataset and 4.6% in detecting objects within synthetic blurry images in our developed small object detection dataset (SOD-Dataset), as well as competitive results compared with other state-of-the-art methods. Meanwhile, ablation experiments and a visualization analysis validate the contributions of each component of the model. |
| format | Article |
| id | doaj-art-68e8ea42bf144470bd78c9f59536f09b |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-68e8ea42bf144470bd78c9f59536f09b2025-02-02T12:48:59ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111112710.1007/s40747-024-01676-wA novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted imagesSayed Jobaer0Xue-song Tang1Yihong Zhang2Gaojian Li3Foysal Ahmed4College of Information Science and Technology, Donghua UniversityCollege of Information Science and Technology, Donghua UniversityCollege of Information Science and Technology, Donghua UniversitySchool of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceCollege of Information Science and Technology, Donghua UniversityAbstract Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. Current models are designed for sharp images, leading to potential detection failures in blurry images. Using image deblurring before object detection is an option, but it demands significant computing power and relies heavily on the accuracy of the deblurring algorithms. Another common issue is the suitable dataset for the specific problem. To address the aforementioned issues, we develop a UAV-assisted small object detection dataset and propose a novel knowledge distillation method for object detection in blurry images in complex environments. Following this, we employ a technique known as self-supervised knowledge distillation, where we introduce a deblurring subnet module with the help of two attention modules, where both networks are trained in a fully-supervised manner. Based on the experiment results, our proposed model achieves an improvement of 4.3% accuracy in the VisDrone synthetic motion blur dataset and 4.6% in detecting objects within synthetic blurry images in our developed small object detection dataset (SOD-Dataset), as well as competitive results compared with other state-of-the-art methods. Meanwhile, ablation experiments and a visualization analysis validate the contributions of each component of the model.https://doi.org/10.1007/s40747-024-01676-wKnowledge distillationBlur detectionObject detectionVisual sentimentDepth-of-focus |
| spellingShingle | Sayed Jobaer Xue-song Tang Yihong Zhang Gaojian Li Foysal Ahmed A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images Complex & Intelligent Systems Knowledge distillation Blur detection Object detection Visual sentiment Depth-of-focus |
| title | A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images |
| title_full | A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images |
| title_fullStr | A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images |
| title_full_unstemmed | A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images |
| title_short | A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images |
| title_sort | novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle assisted images |
| topic | Knowledge distillation Blur detection Object detection Visual sentiment Depth-of-focus |
| url | https://doi.org/10.1007/s40747-024-01676-w |
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