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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Bibliographic Details
Published in:IET image processing Vol. 14; no. 8; pp. 1502 - 1508
Main Authors: Wu, Minghu, Yue, Hanhui, Wang, Juan, Huang, Yongxi, Liu, Min, Jiang, Yuhan, Ke, Cong, Zeng, Cheng
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 19.06.2020
Subjects:
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 Access:Get full text
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Summary: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 methods deteriorates when samples are reduced. To address this, the authors propose an improved Mask R-CNN-based method: the ResNet Group Cascade (RGC) Mask R-CNN. First, they compared ResNet with different layers, finding that ResNeXt-101-64 × 4d is superior to other backbone networks. Secondly, during the training of the test model, the performance of Mask R-CNN suffered from a small batch processing scale, resulting in inaccurately calculated mean and variance; thus, group normalisation was added to the backbone, feature pyramid network neck and bounding box head of the network. Finally, the higher the intersection of Mask R-CNN than the threshold, the easier it is to obtain high-quality samples. However, blindly selecting a high threshold leads to sample reduction and overfitting. Thus, a proposed cascade network configuration with three IoU thresholds was utilised in the process of model training. The model was trained and tested on the COCO and PASCAL VOC07 datasets. Their proposed algorithm demonstrated superior performance compared to that of the Mask R-CNN.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2019.0057