基于双层解耦策略和注意力机制的遮挡目标分割

吕岳; 周浙泉; 吕淑静

doi:10.11999/JEIT211288

基于双层解耦策略和注意力机制的遮挡目标分割

doi: 10.11999/JEIT211288

吕岳^{1, 3, ,},
周浙泉²,
吕淑静^{1, 3}

1.
华东师范大学通信与电子工程学院上海 200062
2.
华东师范大学计算机科学与技术学院上海 200062
3.
上海市多维度信息处理重点实验室上海 200062

详细信息

作者简介:
吕岳：男，博士，教授，研究方向为模式识别、图像处理、智能物联网、机器智能、机器视觉系统

周浙泉：男，硕士生，研究方向为模式识别、图像处理

吕淑静：女，博士，专任研究员，研究方向为模式识别、图像处理、机器学习

通讯作者:
吕岳　ylu@cs.ecnu.edu.cn

中图分类号: TN911.73; TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-18
- 修回日期: 2022-02-24
- 录用日期: 2022-03-01
- 网络出版日期: 2022-03-08
- 刊出日期: 2023-01-17

Occluded Object Segmentation Based on Bilayer Decoupling Strategy and Attention Mechanism

LÜ Yue^{1, 3
, ,},
ZHOU Zhequan²,
LÜ Shujing^{1, 3}

1.
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200062, China
2.
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
3.
Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai 200062, China

摘要

摘要: 遮挡目标分割是实例分割中的一个难点，但在多个应用领域有很强的实用价值，例如物流传输线上堆叠快递包裹的分割。针对快递包裹目标遮挡导致难以分割的问题，该文提出一种基于双层解耦策略和注意力机制的遮挡目标分割方法。该方法首先利用带有特征金字塔(FPN)的主干网络提取图像特征；然后，利用双层解耦检测头自动预测实例的重心是否被遮挡并使用不同的分支对两类不同遮挡类型的实例进行检测；接下来，利用注意力改进模块得到无遮挡实例的预测掩模并将这些掩模合成为一个注意力权重图；最后，注意力改进模块利用该注意力权重图帮助有遮挡实例得到分割结果。该研究采集了一个遮挡快递包裹实例分割数据集，并在该数据集上进行实验。实验结果表明，该方法的平均精度(AP)、召回率(Recall)和漏检率(MR^–2)指标分别达到了95.66%, 97.17%和11.78%，较其他方法具有更优的分割性能。
- 图像分割 /
- 注意力机制 /
- 遮挡目标 /
- 双层解耦策略
Abstract: Occluded object segmentation is a difficult problem in instance segmentation, but it has great practical value in many industrial applications such as stacked parcel segmentation on logistics automatic sorting. In this paper, an occluded object segmentation method based on bilayer decoupling strategy and attention mechanism is proposed to improve the segmentation performance of occluded parcels. Firstly, the image features are extracted through a backbone network with a Feature Pyramid Network (FPN); Secondly, the bilayer decoupling head is used to predict whether the mass centers of instances are occluded, and different occlusion types of instances are predicted through different branches; Thirdly, attention refinement module is used to obtain predicted masks of non-occluded instances and generate an attention map by combining these masks; Finally, this attention map is used to help the prediction of occluded instances. A dataset is provided for occluded parcel segmentation. Our method is tested on this dataset. The experimental results show that the proposed network achieves 95,66% Average Precision(AP), 97.17% Recall, and 11.78% Miss Rate(MR^–2). It indicates that this method has better segmentation performance than other methods.
- Image segmentation /
- Attention mechanism /
- Occluded object /
- Bilayer decoupling strategy

HTML全文

图 1 CondInst的网络结构

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图 2 CondInst的失败案例

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图 3 基于双层解耦策略和注意力机制的CondInst网络结构

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图 4 遮挡快递包裹数据集图像

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图 5 本文方法和基准方法的可视化结果对比

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图 6 注意力改进模块的可视化对比

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表 1 不同方法与不同NMS策略的分割结果(%)

NMS策略	方法	AP	Recall	MR^–2
Box-NMS	Mask RCNN	92.21	93.75	16.79
	CondInst	92.17	93.27	15.96
	CondInst+双层	92.83	94.38	18.96
	CondInst+双层+注意力	94.94	96.00	15.47
Matrix-NMS	Mask RCNN	91.12	91.54	16.94
	CondInst	91.49	92.53	16.01
	CondInst+双层	95.14	96.85	12.75
	CondInst+双层+注意力	95.66	97.17	11.78

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表 2 与遮挡目标检测方法比较的结果(%)

方法	AP	Recall	MR^–2
CondInst+Soft-NMS	94.88	97.67	20.55
CrowdDet	94.22	96.00	17.85
本文方法(基于掩模)	94.72	96.74	14.82
本文方法(基于包围框头)	95.33	97.72	14.38

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表 3 针对有遮挡实例和无遮挡实例的分割结果(%)

方法	无遮挡实例		有遮挡实例
方法	AP	AR	AP	AR
CondInst	94.62	94.70	69.03	77.93
CondInst+双层	96.52	95.59	83.57	91.80
CondInst+双层+注意力	96.62	95.70	84.47	92.52

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表 4 共享卷积层的数量对模型性能的影响

共享卷积层的数量	AP(%)	Recall(%)	MR^–2(%)
4	95.12	97.11	17.45
3	95.22	96.53	12.87
2	95.66	97.17	11.78
1	95.42	96.87	10.87
0	95.32	96.78	11.23

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表 5 不同置信度阈值$ \sigma $对模型性能的影响

置信度阈值$ \sigma $	AP(%)	Recall(%)	MR^–2(%)
0.50	95.27	96.45	12.41
0.60	95.32	96.48	12.10
0.65	95.66	97.17	11.78
0.70	95.31	96.32	12.07
0.90	94.26	95.68	14.79
1.00	94.21	95.60	14.79

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