基于注意力机制的水下遮挡目标检测算法

doi:10.16180/j.cnki.issn1007-7820.2023.05.010

Abstract

Abstract:

In view of the problems of foreground occlusion and background blur in underwater target detection task, an underwater target detection algorithm based on attention mechanism is proposed. Firstly, the image enhancement algorithms are used to improve the image quality. Then, based on the similarity function of the non-local neural network, the concatenation similarity function with logical reasoning capability is fused to enhance the expression ability of the network to the global context features. Additionally, the improved non-local neural network is combined with the triplet attention to make up for the channel features lost by the non-local neural network. Finally, the dilated convolution module is used to replace the pooling operation in triplet attention to reduce the loss of fine-grained information. Experiments show that the proposed algorithm increases the detection accuracy of baseline method from 65.66% to 68.55% on the data set provided by the 2020 National Underwater Target Detection Contest, which proves the effectiveness of the proposed algorithm.

Key words: deep learning, convolutional neural network, underwater target detection, occluded object detection, attention mechanism, similarity function, dilated convolution, Faster R-CNN

CLC Number:

TN957.52

SHI Jianke,QIAO Meiying,LI Bingfeng,ZHAO Yan. Underwater Occlusion Target Detection Algorithm Based on Attention Mechanism[J].Electronic Science and Technology, 2023, 36(5): 62-70.

Figures/Tables 20

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Table 1.

The structure of backbone network"

层数	尺寸	结构
Conv1	300×300×64	k=7×7,s=2
Maxpool	150×150×64	k=3×3,s=2
Layer1	150×150×256	$1 × 1,64 3 × 3,64 1 × 1,256$ ×3
Layer2	75×75×512	$1 × 1,128 3 × 3,128 1 × 1,512$ ×4
Layer3	38×38×1 024	$1 × 1,256 3 × 3,256 1 × 1,1 ? 024$ ×6
Layer4	19×19×2 048	$1 × 1,512 3 × 3,512 1 × 1,2 ? 048$ ×3
Attention	19×19×2 048	Proposed
Avgpool	13×13×2 048	k=7×7

Table 1.

Figure 6.

Figure 7.

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Table 6.

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Table 8.

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Figure 12.

References 23

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程度	数量/张	占比/%
无遮挡	647	12.94
一般遮挡	2 054	41.08
中度遮挡	1 775	35.50
严重遮挡	524	10.48

类型	海胆	扇贝	海星	海参	mAP
图5(a)	75.28	57.54	76.10	57.35	66.57
图5(b)	74.92	58.50	75.87	58.27	66.89
图5(c)	77.14	60.25	77.60	59.20	68.55

方法	海胆	扇贝	海星	海参	mAP
Z-pool	76.55	58.46	76.21	58.68	67.47
DCM	77.14	60.25	77.60	59.20	68.55

方法	海胆	扇贝	海星	海参	漏检率均值
Baseline	48.20	49.52	44.89	54.36	49.25
ResNet-50+NNNet	46.72	54.25	44.33	50.26	48.75
ResNet-50+TA	47.02	50.41	45.68	48.66	47.75
Proposed	45.96	47.88	45.47	49.30	46.50

相似度函数	海胆	扇贝	海星	海参	mAP
Gaussian	74.72	58.10	73.58	55.55	65.49
Embedded Gaussian	75.10	57.02	74.17	56.35	65.66
Dot Product	75.42	56.24	74.03	57.25	65.74
Concatenation	76.30	57.15	74.43	57.34	66.31
Proposed	77.14	60.25	77.60	59.20	68.55

Underwater Occlusion Target Detection Algorithm Based on Attention Mechanism

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方法	主干网络	海胆/%	扇贝/%	海星/%	海参/%	mAP/%	帧率/frames·s^-1
YOLOv3	DarkNet-53	70.42	50.04	71.55	52.70	61.18	27
SSD300	VGG-16	71.72	52.30	72.54	54.72	62.82	22
SSD512	VGG-16	73.20	54.81	73.10	56.37	64.37	18
Faster R-CNN	VGG-16	73.35	54.03	72.52	55.32	63.81	13
Faster R-CNN	ResNet-50	75.10	57.02	74.17	56.35	65.66	12
Faster R-CNN	proposed	77.14	60.25	77.60	59.20	68.55	10

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方法	海胆/%	扇贝/%	海星/%	海参/%	mAP/%	帧率/frames·s^-1
Baseline	75.10	57.02	74.17	56.35	65.66	12
FRANet	74.58	59.86	75.67	55.28	66.35	20
ResNet-50+SENet	73.51	60.10	76.20	58.55	67.09	10
ResNet-50+CBAM	71.34	59.56	72.74	56.36	65.00	9
ResNet-50+NNNet	75.85	58.26	73.35	57.17	66.16	10
ResNet-50+TA	74.24	57.52	74.94	57.55	66.06	12
ResNet-50+NNNet+TA	76.23	58.75	76.25	58.68	67.47	7
Proposed	77.14	60.25	77.60	59.20	68.55	10