Small Object Recognition Algorithm Based on Hybrid Control and Feature Fusion

Drone detection plays a key role in various fields, but from the perspective of drones, factors such as the size of the target, interference from different backgrounds, and lighting affect the detection effect, which can easily lead to missed detections and false detections. To address this problem,...

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Bibliographic Details
Published in:IEEE journal of radio frequency identification (Online) Vol. 8; pp. 484 - 492
Main Authors: Zhu, Gaofeng, Wang, Zhixue, Zhu, Fenghua, Xiong, Gang, Li, Zheng
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerial photography
Algorithms
Attention mechanism
Attention mechanisms
Autonomous aerial vehicles
Boxes
Deep learning
Detection algorithms
drone
Drones
Feature extraction
Hybrid control
Object detection
Object recognition
Radiofrequency identification
Target detection
Unmanned aerial vehicles
YOLO
ISSN:2469-7281, 2469-729X
Online Access:Get full text
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Summary:Drone detection plays a key role in various fields, but from the perspective of drones, factors such as the size of the target, interference from different backgrounds, and lighting affect the detection effect, which can easily lead to missed detections and false detections. To address this problem, this paper proposes a small target detection algorithm. First, the hybrid control of attention mechanism and a convolutional module (HCAC) are used to effectively extract contextual details of targets of different scales, directions, and shapes, while relative position encoding is used to associate targets with position information. Secondly, in view of the small size characteristics of small targets, a high-resolution detection branch is introduced, the large target detection head and its redundant network layers are pruned, and a multi-level weighted feature fusion network (MWFN) is used for multi-dimensional fusion. Finally, the WIoU loss is used as a bounding box regression loss, combined with a dynamic non-monotonic focusing mechanism, to evaluate the quality of anchor boxes so that the detector can handle anchor boxes of different qualities, thus improving the overall performance. Experiments were conducted on the UAV aerial photography data set VisDrone2019. The results showed that the accuracy of P increased by 9.0% and MAP by 9.8%, with higher detection results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2469-7281
2469-729X
DOI:10.1109/JRFID.2024.3384483