Faster R-CNN Target-Detection Algorithm Fused with Adaptive Attention Mechanism

Qisheng Wang; Fengsui Wang; Jingang Chen; Furong Liu

doi:10.3788/LOP202259.1215016

Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1215016(2022)

Faster R-CNN Target-Detection Algorithm Fused with Adaptive Attention Mechanism

Qisheng Wang^1,2,3, Fengsui Wang^1,2,3、*, Jingang Chen^1,2,3, and Furong Liu^1,2,3

Author Affiliations

¹School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, Anhui , China

²Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Wuhu 241000, Anhui , China

³Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Wuhu 241000, Anhui , China

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Figures & Tables(7)

Fig. 1. Structure diagram of MA

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Fig. 2. Comparison diagram of test results. (a) Original algorithm detection results; (b) Detection results of improved algorithm based on MA

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Table 1. Comparison of modified ResNet-50 structure based on MA

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Table 1. Comparison of modified ResNet-50 structure based on MA

Layer name	Output size	ResNet-50	ResNet-50_MA*	ResNet-50_MA
Conv1	112×112	7×7，64，stride 2
Attention 0	112×112			MA
Conv2_x	56×56	3×3 max pool，stride 2
Conv2_x	56×56	$\{\begin{matrix} 1 \times 1, 64 \\ 3 \times 3, 64 \\ 1 \times 1, 256 \end{matrix}\} \times 3$
Conv3_x	28×28	$\{\begin{matrix} 1 \times 1, 128 \\ 3 \times 3, 128 \\ 1 \times 1, 512 \end{matrix}\} \times 4$
Conv4_x	14×14	$\{\begin{matrix} 1 \times 1, 256 \\ 3 \times 3, 256 \\ 1 \times 1, 1024 \end{matrix}\} \times 6$
Conv5_x	7×7	$\{\begin{matrix} 1 \times 1, 512 \\ 3 \times 3, 512 \\ 1 \times 1, 2048 \end{matrix}\} \times 3$
Attention 1	7×7		MA*
	1×1	Average pool，fc，softmax

Table 2. Experimental environment configuration
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Table 2. Experimental environment configuration
Name Model number
System Windows 10
CPU Core i9-10900 @ 3.7 GHz
GPU Nvidia GeForce RTX 2080Ti（11 GB）
Framework Pytorch 1.2.0
Language Python

Table 3. PASCAL VOC dataset training and test data statistics
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Table 3. PASCAL VOC dataset training and test data statistics
Data Trainval Test
Images Objects Images Objects
VOC2007 5011 12608 4952 12032
VOC2012 11540 27450 0 0
Total 16551 40058 4952 12032

Table 4. Comparison of detection accuracy of 20 target classes

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Table 4. Comparison of detection accuracy of 20 target classes

Category	FR	FR+MA*	FR+MA
mAP	74.25	75.01^+0.76	75.16^+0.91
Cat	87.23	87.31^+0.08	89.28^+2.05
Car	86.60	86.33^-0.27	85.85^-0.75
Horse	84.87	86.14^+1.27	86.50^+1.63
Dog	83.62	82.87^-0.75	84.29^+0.67
Bus	80.29	82.55^+2.26	83.45^+3.16
Train	82.49	83.12^+0.63	82.96^+0.47
Motorbike	83.48	83.62^+0.14	83.07^-0.41
Bicycle	79.56	83.14^+3.58	82.72^+3.16
Person	79.75	80.09^+0.34	80.38^+0.63
Aeroplane	79.75	76.09^-3.66	74.28^-5.47
Sheep	75.22	78.26^+3.04	73.20^-2.02
Bird	74.14	76.45^+2.31	77.53^+3.39
Cow	74.73	78.78^+4.05	74.70^-0.03
Tvmonitor	74.34	73.33^-1.01	73.09^-1.25
Diningtable	72.26	71.63^-0.63	73.96^+1.70
Sofa	70.44	75.12^+4.68	73.31^+2.87
Boat	65.69	62.92^-2.77	66.60^+0.91
Chair	54.19	51.53^-2.66	56.31^+2.12
Bottle	52.06	57.03^+4.97	56.48^+4.42
Pottedplant	45.96	43.85^-2.11	45.25^-0.71

Table 5. Comparison of experimental results of ablation of MA

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Table 5. Comparison of experimental results of ablation of MA

Category	FR	FR+MA1	FR+MA2
mAP	74.25	74.61^+0.36	74.70^+0.45
Cat	87.23	87.10^-0.13	90.07^+2.84
Car	86.60	85.94^-0.66	84.66^-1.94
Horse	84.87	85.20^+0.33	85.90^+1.03
Dog	83.62	86.41^+2.79	85.11^+1.49
Bus	80.29	83.17^+2.88	85.36^+5.07
Train	82.49	81.98^-0.51	82.09^-0.40
Motorbike	83.48	81.38^-2.10	81.37^-2.11
Bicycle	79.56	81.80^+2.24	83.38^+3.82
Person	79.75	78.64^-1.11	79.61^-0.14
Aeroplane	79.75	77.68^-2.07	76.20^-3.55
Sheep	75.22	75.62^+0.40	73.97^-1.25
Bird	74.14	75.58^+1.44	75.87^+1.73
Cow	74.73	78.09^+3.36	73.06^-1.67
Tvmonitor	74.34	72.20^-2.14	73.79^-0.55
Diningtable	72.26	70.78^-1.48	70.58^-1.68
Sofa	70.44	74.22^+3.78	73.24^+2.80
Boat	65.69	66.58^+0.89	66.29^+0.66
Chair	54.19	53.49^-0.70	51.81^-2.38
Bottle	52.06	51.17^-0.89	55.37^+3.31
Pottedplant	45.96	45.20^-0.76	46.21^+0.25

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Qisheng Wang, Fengsui Wang, Jingang Chen, Furong Liu. Faster R-CNN Target-Detection Algorithm Fused with Adaptive Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215016

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