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. 2024 Aug 29;14(1):20090.
doi: 10.1038/s41598-024-71001-5.

Adaptive condition-aware high-dimensional decoupling remote sensing image object detection algorithm

Chenshuai Bai  1 , Xiaofeng Bai  1 , Kaijun Wu  1 , Yuanjie Ye  2
Affiliations

Adaptive condition-aware high-dimensional decoupling remote sensing image object detection algorithm

Chenshuai Bai et al. Sci Rep. .

Abstract

Remote Sensing Image Object Detection (RSIOD) faces the challenges of multi-scale objects, dense overlap of objects and uneven data distribution in practical applications. In order to solve these problems, this paper proposes a YOLO-ACPHD RSIOD algorithm. The algorithm adopts Adaptive Condition Awareness Technology (ACAT), which can dynamically adjust the parameters of the convolution kernel, so as to adapt to the objects of different scales and positions. Compared with the traditional fixed convolution kernel, this dynamic adjustment can better adapt to the diversity of scale, direction and shape of the object, thus improving the accuracy and robustness of Object Detection (OD). In addition, a High-Dimensional Decoupling Technology (HDDT) is used to reduce the amount of calculation to 1/N by performing deep convolution on the input data and then performing spatial convolution on each channel. When dealing with large-scale Remote Sensing Image (RSI) data, this reduction in computation can significantly improve the efficiency of the algorithm and accelerate the speed of OD, so as to better adapt to the needs of practical application scenarios. Through the experimental verification of the RSOD RSI data set, the YOLO-ACPHD model in this paper shows very satisfactory performance. The F1 value reaches 0.99, the Precision value reaches 1, the Precision-Recall value reaches 0.994, the Recall value reaches 1, and the mAP value reaches 99.36 % , which indicates that the model shows the highest level in the accuracy and comprehensiveness of OD.

Keywords: Condition awareness technology; High-dimensional decoupling technology; Object detection; Remote sensing image.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
OD algorithm flow chart.
Figure 2
Figure 2
RSOD RSI data sample diagram.
Figure 3
Figure 3
YOLO-ACPHD overall network architecture.
Figure 4
Figure 4
Conditional parameter convolution diagram.
Figure 5
Figure 5
Expert linear mixed diagram.
Figure 6
Figure 6
Expert linear mixed diagram.
Figure 7
Figure 7
F1 value curve.
Figure 8
Figure 8
Precision value curve.
Figure 9
Figure 9
Precision-Recall value curve.
Figure 10
Figure 10
Recall value curve.
Figure 11
Figure 11
Contrast experimental point-line diagram of RSOD RSI.
Figure 12
Figure 12
Ablation experimental point-line diagram of RSOD RSI.
Figure 13
Figure 13
RSOD RSI detection effect diagram.
See this image and copyright information in PMC

References

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