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Water surface object detection using panoramic vision based on improved single-shot multibox detector

In view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection preci...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2021-12, Vol.2021 (1), p.1-15, Article 123
Main Authors: Li, Aofeng, Zhu, Xufang, He, Shuo, Xia, Jiawei
Format: Article
Language:English
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Summary:In view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-021-00831-6