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Article

Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion

1
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
2
Guangdong Feida Transportation Engineering Co., Ltd., Guangzhou 510663, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yunhe Zhao, Jingxiang Xu and Bowen Xing
Sensors 2022, 22(19), 7204; https://doi.org/10.3390/s22197204
Received: 6 August 2022 / Revised: 12 September 2022 / Accepted: 20 September 2022 / Published: 22 September 2022
(This article belongs to the Special Issue Marine Environmental Perception and Underwater Detection)
Aiming at the common problems, such as noise pollution, low contrast, and color distortion in underwater images, and the characteristics of holothurian recognition, such as morphological ambiguity, high similarity with the background, and coexistence of special ecological scenes, this paper proposes an underwater holothurian target-detection algorithm (FA-CenterNet), based on improved CenterNet and scene feature fusion. First, to reduce the model’s occupancy of embedded device resources, we use EfficientNet-B3 as the backbone network to reduce the model’s Params and FLOPs. At the same time, EfficientNet-B3 increases the depth and width of the model, which improves the accuracy of the model. Then, we design an effective FPT (feature pyramid transformer) combination module to fully focus and mine the information on holothurian ecological scenarios of different scales and spaces (e.g., holothurian spines, reefs, and waterweeds are often present in the same scenario as holothurians). The co-existing scene information can be used as auxiliary features to detect holothurians, which can improve the detection ability of fuzzy and small-sized holothurians. Finally, we add the AFF module to realize the deep fusion of the shallow-detail and high-level semantic features of holothurians. The results show that the method presented in this paper yields better results on the 2020 CURPC underwater target-detection image dataset with an AP50 of 83.43%, Params of 15.90 M, and FLOPs of 25.12 G compared to other methods. In the underwater holothurian-detection task, this method improves the accuracy of detecting holothurians with fuzzy features, a small size, and dense scene. It also achieves a good balance between detection accuracy, Params, and FLOPs, and is suitable for underwater holothurian detection in most situations. View Full-Text
Keywords: holothurian; underwater target detection; CenterNet; transformer; scene feature fusion; context information holothurian; underwater target detection; CenterNet; transformer; scene feature fusion; context information
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MDPI and ACS Style

Han, Y.; Chen, L.; Luo, Y.; Ai, H.; Hong, Z.; Ma, Z.; Wang, J.; Zhou, R.; Zhang, Y. Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion. Sensors 2022, 22, 7204. https://doi.org/10.3390/s22197204

AMA Style

Han Y, Chen L, Luo Y, Ai H, Hong Z, Ma Z, Wang J, Zhou R, Zhang Y. Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion. Sensors. 2022; 22(19):7204. https://doi.org/10.3390/s22197204

Chicago/Turabian Style

Han, Yanling, Liang Chen, Yu Luo, Hong Ai, Zhonghua Hong, Zhenling Ma, Jing Wang, Ruyan Zhou, and Yun Zhang. 2022. "Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion" Sensors 22, no. 19: 7204. https://doi.org/10.3390/s22197204

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