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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Published in:	Remote sensing (Basel, Switzerland) Vol. 15; no. 18; p. 4580
Main Authors:	Wang, Yuming, Zou, Hua, Yin, Ming, Zhang, Xining
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.09.2023
Subjects:	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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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Zou, Hua Wang, Yuming Zhang, Xining Yin, Ming
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SubjectTerms	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
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Title	SMFF-YOLO: A Scale-Adaptive YOLO Algorithm with Multi-Level Feature Fusion for Object Detection in UAV Scenes
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