Abstract
Dealing with the insufficient detection accuracy and speed of aircraft targets in remote sensing images under complex background, this paper proposes a new detection method, YOLOv5-Aircraft, based on the YOLOv5 network. The YOLOv5-Aircraft model is improved in 3 ways: (1) At the beginning and end of original batch normalization module, centering and scaling calibration are added to enhance the effective features and form a more stable feature distribution, which strengthens the feature extraction ability of network model. (2) The cross-entropy loss function in the confidence of the original loss function is improved to the loss function based on smoothed Kullback-Leibler divergence. (3) For reducing information loss, the CSandGlass module is designed on the backbone feature extraction network of YOLOv5 to replace the residual module. Meanwhile, low-resolution feature layers are eliminated to reduce semantic loss. Experiment results demonstrate that the YOLOv5-Aircraft model can enhance the accuracy and speed of aircraft target detection in remote sensing images while achieving easier convergence.
Highlights
With the continuous development of satellite remote sensing technology, the information amount of high-resolution remote sensing images has increased sharply, and the detailed information contained in is getting more abundant
Experiment results demonstrate that the YOLOv5-Aircraft model can enhance the accuracy and speed of aircraft target detection in remote sensing images while achieving easier convergence
This paper presents a network model, YOLOv5-Aircraft, based on improved YOLOv5 to enhance the detection accuracy and detection speed of aircraft targets in remote sensing images
Summary
OF YOLOv5 DETECTION NETWORK YOLOv5 proposed by Ultralytics LLC is an improved version based on YOLOv4. Input includes three parts: mosaic data enhancement, adaptive anchor frame calculation and adaptive image scaling. Neck uses FPN (feature pyramid networks) and PAN (pyramid attention network) structure, and its structure is shown in figure 3. In order to perform feature correction in BN, centering and scaling calibration were added at the beginning and end of the original. To utilize the optimization of batch normalization in existing deep learning frameworks, we add the centering and scaling calibrations at the beginning and ending of the original normalization layer of batch normalization, respectively, which enhances the effective features and forms a more stable feature distribution, and enhances the feature extraction ability of the network model. It can be seen that the feature map on the far right after the centering and scaling calibration is more significant than the original feature map output in the middle
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