Deep Learning Approach For Objects Detection in Underwater Pipeline Images
Author:
Affiliation:
1. Faculty of Engineering, University of Rijeka, RIjeka, Croatia
2. Center for Artificial Intelligence and Cybersecurity, University of Rijeka, RIjeka, Croatia
3. Faculty of Informatics and Digital Technologies, University of Rijeka, RIjeka, Croatia
Funder
Croatian Science Foundation under the project
EU Horizon
Croatian舑Slovenian bilateral
Croatian IRI2
University of Rijeka
Publisher
Informa UK Limited
Subject
Artificial Intelligence
Link
https://www.tandfonline.com/doi/pdf/10.1080/08839514.2022.2146853
Reference38 articles.
1. CNN-based YOLOv3 Comparison for Underwater Object Detection
2. Bochkovskiy, A., C.Y. Wang, and H.Y.M Liao. 2020. “Yolov4: Optimal speed and accuracy of object detection.” arXiv preprint arXiv:2004.10934.
3. Underwater Target Recognition Based on Improved YOLOv4 Neural Network
4. Fast R-CNN
5. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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