规范化(社会学)
计算机科学
聚类分析
最小边界框
目标检测
人工智能
棱锥(几何)
模式识别(心理学)
跳跃式监视
特征提取
特征(语言学)
卷积神经网络
代表(政治)
图像(数学)
数学
哲学
社会学
几何学
政治
语言学
法学
人类学
政治学
作者
Han Weiyue,Liu Xiao-hong
标识
DOI:10.1109/icaica50127.2020.9182521
摘要
Object detection has made impressive progress in recent years where Faster R-CNN is the mainstream framework for region-based object detection methods. However, a single Faster R-CNN framework no longer has advantages compared with the latest detection models. So based on Faster R-CNN, a model that focuses on features, normalization methods, and anchor sizes is proposed to improve detection results. The model integrates Feature Pyramid Networks (FPN), Group Normalization (GN) with k-means clustering. FPN is used to produce a multi-scale feature representation, which enables the model to detect objects across a wide range of scales. GN addresses the problem of the small training batch size effectively. K-means clustering algorithm is used finally to determine anchor sizes of the network on the purpose of making the network do bounding-box regression more easily. Without bells and whistles, the detection model achieves state-of-the-art object detection accuracy on the MSCOCO datasets.
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