A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle
Unexploded ordnance (UXO) presents a significant risk to both the natural environment and human safety. Current deep learning detection mechanisms are characterized by limited interpretability, resulting in a persistent absence of detection methods that are simultaneously efficient, secure, and prec...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858135/ |
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| author | Zhongao Ling Hui Zhao Xu Zhao Ziyu Liu Wenbin Chen |
| author_facet | Zhongao Ling Hui Zhao Xu Zhao Ziyu Liu Wenbin Chen |
| author_sort | Zhongao Ling |
| collection | DOAJ |
| description | Unexploded ordnance (UXO) presents a significant risk to both the natural environment and human safety. Current deep learning detection mechanisms are characterized by limited interpretability, resulting in a persistent absence of detection methods that are simultaneously efficient, secure, and precise. To address these challenges, a dual-mode detection method based on interpretation-promoted YOLOv5 is proposed in this paper. A comprehensive dataset has been constructed to focus on the scarcity of low-altitude UXO target datasets, incorporating both visible light and infrared imagery. Dataset augmentation has been implemented through the application of generative adversarial networks for image super-resolution reconstruction, thereby enhancing the robustness of the dataset. To tackle issues of low detection accuracy and inadequate background discrimination associated with a single information source, an integration of visible light and infrared data has been proposed to enhance the interpretability of the YOLOv5 algorithm, leading to improved detection performance. Extensive low-altitude detection experiments were conducted in field environments using unmanned aerial vehicles (UAVs). Experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 97.1% and an impressive detection speed of up to 60.3 frames per second. |
| format | Article |
| id | doaj-art-75a56a68e2284a49becb92c9cd7fe523 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-75a56a68e2284a49becb92c9cd7fe5232025-08-20T02:04:30ZengIEEEIEEE Access2169-35362025-01-0113426344264910.1109/ACCESS.2025.353705810858135A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial VehicleZhongao Ling0https://orcid.org/0009-0002-7054-767XHui Zhao1https://orcid.org/0000-0002-5326-9930Xu Zhao2https://orcid.org/0000-0002-1712-6588Ziyu Liu3https://orcid.org/0009-0006-4401-713XWenbin Chen4Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing, ChinaBeijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing, ChinaBeijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing, ChinaSchool of Environmental Science and Engineering, Beijing University of Technology, Beijing, ChinaChina Electronics Technology Group Corporation, Beijing, ChinaUnexploded ordnance (UXO) presents a significant risk to both the natural environment and human safety. Current deep learning detection mechanisms are characterized by limited interpretability, resulting in a persistent absence of detection methods that are simultaneously efficient, secure, and precise. To address these challenges, a dual-mode detection method based on interpretation-promoted YOLOv5 is proposed in this paper. A comprehensive dataset has been constructed to focus on the scarcity of low-altitude UXO target datasets, incorporating both visible light and infrared imagery. Dataset augmentation has been implemented through the application of generative adversarial networks for image super-resolution reconstruction, thereby enhancing the robustness of the dataset. To tackle issues of low detection accuracy and inadequate background discrimination associated with a single information source, an integration of visible light and infrared data has been proposed to enhance the interpretability of the YOLOv5 algorithm, leading to improved detection performance. Extensive low-altitude detection experiments were conducted in field environments using unmanned aerial vehicles (UAVs). Experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 97.1% and an impressive detection speed of up to 60.3 frames per second.https://ieeexplore.ieee.org/document/10858135/Unexploded ordnancelow-altitude detectionvisible lightinfrareddual-mode fusionYOLOv5 |
| spellingShingle | Zhongao Ling Hui Zhao Xu Zhao Ziyu Liu Wenbin Chen A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle IEEE Access Unexploded ordnance low-altitude detection visible light infrared dual-mode fusion YOLOv5 |
| title | A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle |
| title_full | A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle |
| title_fullStr | A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle |
| title_full_unstemmed | A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle |
| title_short | A Dual Mode Detection Method for Unexploded Ordnance Based on YOLOv5 for Low Altitude Unmanned Aerial Vehicle |
| title_sort | dual mode detection method for unexploded ordnance based on yolov5 for low altitude unmanned aerial vehicle |
| topic | Unexploded ordnance low-altitude detection visible light infrared dual-mode fusion YOLOv5 |
| url | https://ieeexplore.ieee.org/document/10858135/ |
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