Ships’ Small Target Detection Based on the CBAM-YOLOX Algorithm

In order to solve the problem of low accuracy of small target detection in traditional target detection algorithms, the YOLOX algorithm combined with Convolutional Block Attention Module (CBAM) is proposed. The algorithm first uses CBAM on the shallow feature map to better focus on small target info...

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Bibliographic Details
Published in:Journal of marine science and engineering 2022-12, Vol.10 (12), p.2013
Main Authors: Wang, Yuchao, Li, Jingdong, Chen, Zeming, Wang, Chenglong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alliances
attention mechanism
convolutional neural network
deep learning
Detection
Feature maps
Neural networks
Semantics
Ships
small target detection
Target detection
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Summary:In order to solve the problem of low accuracy of small target detection in traditional target detection algorithms, the YOLOX algorithm combined with Convolutional Block Attention Module (CBAM) is proposed. The algorithm first uses CBAM on the shallow feature map to better focus on small target information, and the Focal loss function is used to regress the confidence of the target to overcome the positive and negative sample imbalance problem of the one-stage target detection algorithm. Finally, the Soft Non-Maximum Suppression (SNMS) algorithm is used for post-processing to solve the problem of missed detection in close range ship target detection. The experimental results show that the average accuracy of the proposed CBAM-YOLOX network target detection is improved by 4.01% and the recall rate is improved by 8.81% compared with the traditional YOLOX network, which verifies the effectiveness of the proposed algorithm.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse10122013