SMFF-YOLO: A Scale-Adaptive YOLO Algorithm with Multi-Level Feature Fusion for Object Detection in UAV Scenes

Object detection in images captured by unmanned aerial vehicles (UAVs) holds great potential in various domains, including civilian applications, urban planning, and disaster response. However, it faces several challenges, such as multi-scale variations, dense scenes, complex backgrounds, and tiny-s...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Jg. 15; H. 18; S. 4580
Hauptverfasser: Wang, Yuming, Zou, Hua, Yin, Ming, Zhang, Xining
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2023
Schlagworte:
Accuracy
Adaptability
Adaptive algorithms
algorithms
Comparative analysis
Competitiveness
complex scenarios
data collection
Datasets
Deep learning
Disaster management
Drone aircraft
head
Image segmentation
Localization
Machine learning
Machine vision
Methods
Modules
multi-level feature information fusion
Neural networks
object detection
Object recognition
Performance evaluation
Photography, Aerial
prediction
Remote sensing
Technology application
Telematics
tiny objects
Unmanned aerial vehicles
Urban planning
China
ISSN:2072-4292, 2072-4292
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Zusammenfassung:Object detection in images captured by unmanned aerial vehicles (UAVs) holds great potential in various domains, including civilian applications, urban planning, and disaster response. However, it faces several challenges, such as multi-scale variations, dense scenes, complex backgrounds, and tiny-sized objects. In this paper, we present a novel scale-adaptive YOLO framework called SMFF-YOLO, which addresses these challenges through a multi-level feature fusion approach. To improve the detection accuracy of small objects, our framework incorporates the ELAN-SW object detection prediction head. This newly designed head effectively utilizes both global contextual information and local features, enhancing the detection accuracy of tiny objects. Additionally, the proposed bidirectional feature fusion pyramid (BFFP) module tackles the issue of scale variations in object sizes by aggregating multi-scale features. To handle complex backgrounds, we introduce the adaptive atrous spatial pyramid pooling (AASPP) module, which enables adaptive feature fusion and alleviates the negative impact of cluttered scenes. Moreover, we adopt the Wise-IoU(WIoU) bounding box regression loss to enhance the competitiveness of different quality anchor boxes, which offers the framework a more informed gradient allocation strategy. We validate the effectiveness of SMFF-YOLO using the VisDrone and UAVDT datasets. Experimental results demonstrate that our model achieves higher detection accuracy, with AP50 reaching 54.3% for VisDrone and 42.4% for UAVDT datasets. Visual comparative experiments with other YOLO-based methods further illustrate the robustness and adaptability of our approach.
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SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15184580