YOLO-extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images

Compared with natural images, remote sensing targets have small and dense target shapes as well as complex target backgrounds. As a result, insufficient detection accuracy and target location cannot be accurately identified. So, this paper proposes the YOLO-extract algorithm based on the YOLOv5 algo...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Liu, Zhiguo, Gao, Yuan, Du, Qianqian, Chen, Meng, Lv, Wenqiang
Format: Article
Language:English
Subjects:
Aircraft
Aircraft detection
Algorithms
Atmospheric modeling
Convolution
Dilated Convolution
Feature extraction
Focal-α IoU Loss
Information retrieval
Object detection
Object recognition
Regression models
Remote sensing
Remote Sensing Aircraft Target
Semantics
Structure Optimization
Target detection
Targeting
YOLOv5
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Summary:Compared with natural images, remote sensing targets have small and dense target shapes as well as complex target backgrounds. As a result, insufficient detection accuracy and target location cannot be accurately identified. So, this paper proposes the YOLO-extract algorithm based on the YOLOv5 algorithm. Firstly, The YOLO-extract algorithm optimized the model structure of the YOLOv5 algorithm. The YOLO-extract algorithm not only deleted the feature layer and prediction head with poor feature extraction ability but also a new feature extractor with stronger feature extraction ability was integrated into the network. At the same time, YOLO-extract borrowed the idea of residual network to integrate Coordinate Attention into the network. Secondly, the mixed dilated convolution was combined with the redesigned residual structure to enhance the feature and location information extraction ability of the shallow layer of the model and optimize the feature extraction ability of the model for different scale targets. Finally, drawing on the idea of α-IoU Loss, Focal-α EIoU Loss was designed to replace CIoU Loss, which makes the model bounding box regression faster and the loss lower. The experimental results on the test data set show that compared with the YOLOv5 algorithm, the YOLO-extract algorithm has a faster convergence speed, reduces the calculation amount by 45.3GFLOPs and the number of parameters by 10.526M, but increases the mAP by 8.1% and the detection speed by 3 times.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3233964