ULO: An Underwater Light-Weight Object Detector for Edge Computing

Recent studies on underwater object detection have progressed with the development of deep-learning methods. Generally, the model performance increase is accompanied by an increase in computation. However, a significant fraction of remotely operated underwater vehicles (ROVs) and autonomous underwat...

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Bibliographic Details
Published in:Machines (Basel) 2022-08, Vol.10 (8), p.629
Main Authors: Wang, Lin, Ye, Xiufen, Wang, Shunli, Li, Peng
Format: Article
Language:English
Subjects:
Ablation
adaptive pre-processing
Algorithms
Annotations
Autonomous underwater vehicles
Datasets
Design
Detectors
Edge computing
Experiments
Image degradation
Machine learning
Modules
Neural networks
object detection
Object recognition
Remote submersibles
Remotely operated vehicles
Scallops
underwater
Underwater light
Vehicles, Remotely piloted
Weight reduction
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Summary:Recent studies on underwater object detection have progressed with the development of deep-learning methods. Generally, the model performance increase is accompanied by an increase in computation. However, a significant fraction of remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) operate in environments with limited power and computation resources, making large models inapplicable. In this paper, we propose a fast and compact object detector—namely, the Underwater Light-weight Object detector (ULO)—for several marine products, such as scallops, starfish, echinus, and holothurians. ULO achieves comparable results to YOLO-v3 with less than 7% of its computation. ULO is modified based on the YOLO Nano architecture, and some modern architectures are used to optimize it, such as the Ghost module and decoupled head design in detection. We propose an adaptive pre-processing module for the image degradation problem that is common in underwater images. The module is lightweight and simple to use, and ablation experiments verify its effectiveness. Moreover, ULO Tiny, a lite version of ULO, is proposed to achieve further computation reduction. Furthermore, we optimize the annotations of the URPC2019 dataset, and the modified annotations are more accurate in localization and classification. The refined annotations are available to the public for research use.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines10080629