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引用次数: 0

摘要

图像中物体的分类和定位是刺激人工神经网络研究的最初突破之一。提高检测精度和其他指标一直是一个持续的研究目标和分析主题。在训练这些模型的过程中,一个特别的问题是输入图像的不同分辨率,它对训练/检测结果的影响,因为大多数模型接受任意分辨率并在内部将其调整为特定分辨率。本文研究了在检测阶段之前自动将图像分割成小块,然后将这些小块与检测结果一起加入原始图像的好处。在实际环境中进行了精度测试。
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Improving the Accuracy of Object Detection Models using Patch Splitting
The classification and afterwards also the localization of objects in images were among the initial breakthroughs that stimulated research into artificial neural networks. Improving the detection accuracy along with other metrics has been a continuous research objective and analysis subject. One particular issue during the training of such models is the varying resolution of input images and it's effects on the training/detection results as most models accept arbitrary resolutions and resize them internally to a specific resolution. This article examines the benefits of automatically splitting images into patches before the detection stage and afterwards joining the patches along with the detection results into the original image. An accuracy test is conducted in a real environment.
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