基于注意力机制和图像轮廓的实例分割算法

doi:10.16180/j.cnki.issn1007-7820.2024.04.009

Abstract

Abstract:

Based on image contour, the instance segmentation method uses fewer contour nodes to represent an object, which effectively reduces the number of algorithmic parameters and improves its operation efficiency. However, with the segmentation result of poor quality, it is no match for traditional pixel-by-pixel processing segmentation algorithm in terms of accuracy. To improve the accuracy of the algorithm, it is of great necessity to introduce a refined model of the instance segmentation (Attend the Contour snake,AC-snake), which is based on image contour with a combination of attention mechanism. An improved Largekernel+ is added to the backbone network to improve the receptive field of the model and extract richer feature information. The network structure at the contour vertex deformation stage is improved, and the Dual Channel attention (DC-attentio) module is combined to enhance the effective information of contour vertex, reduce the invalid parameters in the training network, and improve the detection accuracy and training speed. The experimental results show that in Cityscapes validation data set, the improved model proposed in this study has improved performance when compared with the original model.

Key words: instance segmentation, image contour, contour node, pre-pixel, attention meachanism, large kernel, receptive field, feature information

CLC Number:

TN247

GU Denghua, GU Chunhua. Instance Segmentation Based on Attention and Image Contour[J].Electronic Science and Technology, 2024, 37(4): 62-68.

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References 20

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参数	数值	参数	数值
数据集	Cityscapes	K	128
Num	5 000	Epoch	200
Momentum	0.9	Learning Rate	0.000 1
Optimizer	Adam	Batch size	16

模型	SE	DC-attention	Largekernel+	mAP50/%	AP/%
模型1				59.4	33.3
模型2	√			60.8	33.4
模型3		√		60.6	33.7
模型4			√	61.1	33.8
模型5	√		√	61.9	34.3
模型6		√	√	62.3	34.4

模型	person/%	rider/%	car/%	truck/%
Baseline	37.3	27.0	56.3	32.1
Baseline+SE	36.6	27.0	56.4	32.5
Baseline+DC	37.7	28.7	57.0	33.6
Baseline+DC+Largekernel	37.4	27.6	57.0	33.8

模型	bus/%	train/%	motorcycle/%	bicycle/%
Baseline	54.1	20.8	19.0	19.4
Baseline+SE	53.8	24.3	18.6	17.5
Baseline+DC	54.8	19.9	17.6	19.8
Baseline+DC+Largekernel	54.9	27.1	19.0	18.7%

模型	training data	输入尺寸	AP/%	mAP50/%
Mask R-CNN	fine	1 024×2 048	26.2	49.9
PANet	fine	1 024×2 048	31.8	57.1
Polarmask	fine	1 024×2 048	30.5	56.9
DeepSnake	fine	1 024×2 048	31.7	58.4
本文	fine	1 024×2 048	31.9	58.9

Instance Segmentation Based on Attention and Image Contour

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模型	person/%	rider/%	car/%	truck/%
Mask R-CNN	30.5	23.7	46.9	22.8
PANet	36.8	30.4	54.8	27.0
Polarmask	35.6	24.7	55.5	29.7
DeepSnake	37.2	27.0	56.0	29.5
AC-snake	37.0	26.6	56.1	30.1

模型	bus/%	train/%	motorcycle/%	bicycle/%
Mask R-CNN	32.2	18.6	19.1	16.0
PANet	36.3	25.5	22.6	20.8
Polarmask	41.1	25.7	16.3	15.4
DeepSnake	40.5	19.9	19.0	16.4
AC-snake	42.1	26.0	18.9	18.4

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