Underwater Object Detection Based on Improved EfficientDet

Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater....

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-09, Vol.14 (18), p.4487
Main Authors: Jia, Jiaqi, Fu, Min, Liu, Xuefeng, Zheng, Bing
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
convolutional neural network
Datasets
Deep learning
Efficiency
EfficientDet
Feature extraction
Intelligence
Marine environment
marine organism
Marine organisms
Modules
Neural networks
object detection
Object recognition
Organisms
Remote sensing
Underwater
Underwater robots
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Summary:Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14184487