Underwater Object Detection Based on Improved EfficientDet
TL;DR: Wang et al. as discussed by the authors reconstructed the MBConvBlock by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer and removed the fully connected layer of the attention module and convolution is used to cut down the amount of network parameters.
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Abstract: 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.
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Citations
Augmented weighted bidirectional feature pyramid network for marine object detection
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TL;DR: This paper proposes AWBiFPN, a novel network for marine object detection, improving performance by reducing feature weakening and enhancing multi-scale feature integration, achieving 81.63% mAP on UTDAC and 37.9 AP on MS COCO benchmark.
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TL;DR: This study proposes YWnet, a convolutional block attention-based fusion deep learning method for complex underwater small target detection, achieving 73.2% mAp and 39.3% mAp50–95 on the underwater dataset, outperforming YOLOv5 and nine state-of-the-art baseline models.
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Shaolin Qu,Can Cui,Jiale Duan,Yongling Lu,Zilong Pang +4 more
TL;DR: This work proposes a new neural network model, YOLOv8-LA, and develops the AP-FasterNet architecture for small targets that are commonly found in underwater datasets, demonstrating the model’s ability to ensure high detection accuracy while maintaining real-time processing capabilities.
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CEH-YOLO: A composite enhanced YOLO-based model for underwater object detection
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Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques
Salvador López-Barajas,Pedro J. Sanz,Raúl Marín-Prades,Alfonso Gómez-Espinosa,Josué González-García,Juan Echagüe +5 more
TL;DR: A hybrid underwater intervention system using deep learning techniques is proposed for fish net cage inspection, incorporating computer vision, object detection, and trajectory control to detect holes and estimate net distance, demonstrating robustness and viability in experimental validation.
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