Object detection based on RGC mask R-CNN

Object detection is a crucial topic in computer vision. Mask Region-Convolution Neural Network (R-CNN) based methods, wherein a large intersection over union (IoU) threshold is chosen for high quality samples, have often been employed for object detection. However, the detection performance of such...

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Veröffentlicht in:	IET image processing Jg. 14; H. 8; S. 1502 - 1508
Hauptverfasser:	Wu, Minghu, Yue, Hanhui, Wang, Juan, Huang, Yongxi, Liu, Min, Jiang, Yuhan, Ke, Cong, Zeng, Cheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 19.06.2020
Schlagworte:	bounding box head computer vision detection performance feature extraction feature pyramid network neck high quality samples image classification image coding image representation improved Mask R‐CNN‐based method learning (artificial intelligence) Mask Region‐Convolution Neural Network based methods neural nets object detection Research Article ResNet Group Cascade Mask R‐CNN RGC mask R‐CNN image classification detection performance improved Mask R-CNN-based method object detection high quality samples bounding box head feature extraction ResNet Group Cascade Mask R-CNN computer vision image representation RGC mask R-CNN feature pyramid network neck Mask Region-Convolution Neural Network based methods learning (artificial intelligence) high-quality samples image coding neural nets
ISSN:	1751-9659, 1751-9667
Online-Zugang:	Volltext
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