AIGDet: Altitude-Information-Guided Vehicle Target Detection in UAV-Based Images
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| Publicado en: | IEEE Sensors Journal vol. 24, no. 14 (2024), p. 22672 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| Materias: | |
| Acceso en línea: | Citation/Abstract |
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| 001 | 3081870500 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1530-437X | ||
| 022 | |a 1558-1748 | ||
| 024 | 7 | |a 10.1109/JSEN.2024.3406540 |2 doi | |
| 035 | |a 3081870500 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 121631 |2 nlm | ||
| 100 | 1 | |a Yang, Ziqin |u School of Electrical Engineering and Automation, Wuhan University, Wuhan, China | |
| 245 | 1 | |a AIGDet: Altitude-Information-Guided Vehicle Target Detection in UAV-Based Images | |
| 260 | |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Conducting extensive vehicle detection through the high-altitude perspective offered by unmanned aerial vehicles (UAVs) poses significant challenges. The high-altitude operation of UAVs to acquire a broader reconnaissance view results in low-resolution and densely packed vehicle targets in the captured imagery, creating substantial difficulties for vehicle detection. To address this, we propose a vehicle detection network specifically designed for UAVs, incorporating an end-to-end network that takes scale consistency constraints into consideration. The cornerstone of our method is the dynamic feature refinement module (DFRM), designed to overcome the feature attenuation and limitations in utilizing high-level prior information common in traditional approaches. Initially, we developed an adaptive target suggestion module based on the prior characteristics of the targets and scenes, and the scale consistency hypothesis of similar vehicles at different UAV flying altitudes. This module optimizes the number and scale of anchors by introducing prior information, facilitating preliminary localization of small-scale imaging targets. Subsequently, we constructed a multilayer feature purification structure based on a feature pyramid network (FPN) to refine bounding boxes at each level with height prior, integrating additional contextual information. This approach allows us to utilize more contextual information for vehicle detection while enhancing localization accuracy through detailed height prior. Our application and evaluation on multiple open-source datasets with height labels demonstrate that our method, with minimal parameter introduction, achieves excellent mean average precision (mAP) value. This underscores the effectiveness of our approach in UAV-based vehicle detection. | |
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Image acquisition | ||
| 653 | |a High altitude | ||
| 653 | |a Image resolution | ||
| 653 | |a Modules | ||
| 653 | |a Target acquisition | ||
| 653 | |a Localization | ||
| 653 | |a Multilayers | ||
| 653 | |a Altitude | ||
| 653 | |a Vehicles | ||
| 653 | |a Target detection | ||
| 700 | 1 | |a Xie, Fuxin |u School of Electrical Engineering and Automation, Wuhan University, Wuhan, China | |
| 700 | 1 | |a Zhou, Jian |u State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China | |
| 700 | 1 | |a Yao, Yuan |u State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) and the Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China | |
| 700 | 1 | |a Hu, Cheng |u College of Computer Technology, Wuhan Institute of Shipbuilding Technology, Wuhan, China | |
| 700 | 1 | |a Zhou, Baoding |u College of Civil and Transportation Engineering and Institute of Urban Smart Transportation and Safety Maintenance, Shenzhen University, Shenzhen, China | |
| 773 | 0 | |t IEEE Sensors Journal |g vol. 24, no. 14 (2024), p. 22672 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3081870500/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |