Academic Journal of Engineering and Technology Science, 2023, 6(5); doi: 10.25236/AJETS.2023.060504.
Peilin Li
College of Art and Science, The Ohio State University, Columbus, Ohio, 43201, United State
Ship target detection plays an important role in marine military and shipping. Deep learning allows us to extract deep features from large amount of data. In this paper, we select three different target detection algorithms based on deep learning, including Faster R-CNN, SSD, and YOLOv3, and apply the same dataset to these three algorithms. Then compare the results of the experiments and evaluate the performance of each algorithm. According to the result of the experiments, Faster R-CNN has a relatively better performance. The result of this paper would provide a reference for selecting a ship target detection algorithm.
Deep Learning, Ship Target Detection, Convolutional Neural Network, Faster R-CNN, SSD, YOLOv3
Peilin Li. Performance Evaluation of Ship Target Detection Algorithms Based on Deep Learning. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 5: 17-22. https://doi.org/10.25236/AJETS.2023.060504.
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