Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN

GU Jiaojiao; LI Bingzhen; LIU Ke; JIANG Wenzhi

Volume 43 Issue 2

Mar. 2021

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Article Navigation > Infrared Technology > 2021 > 43(2): 170-178

GU Jiaojiao, LI Bingzhen, LIU Ke, JIANG Wenzhi. Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN[J]. Infrared Technology , 2021, 43(2): 170-178.

Citation:

GU Jiaojiao, LI Bingzhen, LIU Ke, JIANG Wenzhi. Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN[J]. Infrared Technology , 2021, 43(2): 170-178.

GU Jiaojiao, LI Bingzhen, LIU Ke, JIANG Wenzhi. Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN[J]. Infrared Technology , 2021, 43(2): 170-178.

Citation:

GU Jiaojiao, LI Bingzhen, LIU Ke, JIANG Wenzhi. Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN[J]. Infrared Technology , 2021, 43(2): 170-178.

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Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN

1.
Naval Aviation University, Coast Guard Academy, Yantai 264001, China
2.
Unit 95668 People's Liberation Army of China, Kunming 650000, China

Received Date: 2020-06-11
Rev Recd Date: 2020-07-06
Publish Date: 2021-02-20

Abstract

Abstract

To solve the problem of insufficient feature extraction and repeated detection of infrared ship targets by the Faster R-CNN algorithm, a ship target detection algorithm based on an improved Faster R-CNN is proposed. First, three feature graphs are drawn from the backbone network, VGG-16, after a three-segment convolution, and the features are spliced to form a multi-scale feature graph to obtain a feature vector with richer semantic information; second, the Anchor is improved based on the dataset, and the number and size of the Anchor boxes are reset; finally, the loss function of the improved Faster R-CNN is optimized to improve the feature extraction ability of the target. An analysis of the experimental results on the test dataset demonstrates that the average accuracy of the improved detection algorithm was 83.98%, which is 3.95% higher than that of the original Faster RCNN.
- deep learning,
- target detection,
- ship target,
- infrared image,
- Faster R-CNN

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References(18)

References

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