基于改进的带变压器YOLO的皮艇和帆船检测

Ziyuan Luo, Wei Yan, Minh Nguyen
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引用次数: 2

摘要

在本文中,我们的目标是使用深度学习从数字图像中检测帆船和皮艇。主数据集是我们自己创建的,我们在我们的城市奥克兰附近的港口收集了图像。在帆船和皮艇检测中,我们为YOLOv5模型的基线寻找一组最佳参数。在本文中,我们提出了大量的骨干结构用于比较,我们能够使用我们的训练数据集找到kayak检测的最佳结构。最后,我们验证了我们提出的模型,并使用集成学习将其与现有的训练良好的模型进行了比较。所有结果都是在GTX1060 6G RAM GPU的计算机上获得的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kayak and Sailboat Detection Based on the Improved YOLO with Transformer
In this paper, we aim at sailboat and kayak detection from digital images using deep learning. The main dataset is created by ourselves, we have collected the images at the harbour near our city, Auckland. In the sailboat and kayak detection, we search for a set of best parameters for the baseline of YOLOv5 model. In this paper, we propose a spate of backbone structures for the purpose of comparisons, we are able to find out the best structure for the kayak detection using our training dataset. Finally, we verify our proposed model and compare it with an existing well-trained model by using ensemble learning. All results are obtained based on a computer with GTX1060 6G RAM GPU.
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