Detection Method of Downpipe Diseases Based on Visual Attention Mechanism

Jiasong Zhu; Tianzhu Ma; Haokun Yang; Xu Fang; Qing Li

doi:10.3788/LOP202259.1815001

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 18 >Page 1815001 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 18, 1815001 (2022)

Detection Method of Downpipe Diseases Based on Visual Attention Mechanism

Jiasong Zhu^1、2、**, Tianzhu Ma^1、3、***, Haokun Yang^1、3, Xu Fang^2、4, and Qing Li^1、*

Author Affiliations

¹Institute of Urban Smart Transportation & Safety Maintenance, Shenzhen 518000, Guangdong , China

²Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, Guangdong , China

³College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518000, Guangdong , China

⁴College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, Guangdong , China

show less

DOI: 10.3788/LOP202259.1815001 Cite this Article Set citation alerts

Jiasong Zhu, Tianzhu Ma, Haokun Yang, Xu Fang, Qing Li. Detection Method of Downpipe Diseases Based on Visual Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815001 Copy Citation Text

show less

Fig. 1. Residual attention model network diagram

Download full size

Fig. 2. Schematic diagram of fusion strategy

Download full size

Style	Precision	Recall	F1	Number of samples
Broken	0.82	0.78	0.80	400
Corrosion	0.89	0.90	0.90	400
Deformation	0.88	0.86	0.87	400
Normal	0.92	0.97	0.95	400

Table 1. Results of different defects under shallow fusion strategy

Style	Precision	Recall	F1	Number of samples
Broken	0.82	0.73	0.77	400
Corrosion	0.88	0.94	0.91	400
Deformation	0.84	0.80	0.82	400
Normal	0.89	0.97	0.93	400

Table 2. Results of different defects under middle fusion strategy

Style	Precision	Recall	F1	Number of samples
Broken	0.86	0.86	0.86	400
Corrosion	0.88	0.91	0.89	400
Deformation	0.87	0.83	0.85	400
Normal	0.98	0.99	0.98	400

Table 3. Results of different defects under bottom fusion strategy

Neural network	fusion strategy	F1	accuracy	recall
Residual	Shallow fusion	0.860	0.859	0.859
Attention	Middle fusion	0.878	0.879	0.878
Network	Bottom fusion	0.895	0.896	0.896

Table 4. Results of different fusion strategies

Neural network	F1	Accuracy	Recall
Residual Attention Network	0.895	0.896	0.896
Resnet50	0.874	0.876	0.875

Table 5. Results of different neural networks based on bottom fusion strategy

Jiasong Zhu, Tianzhu Ma, Haokun Yang, Xu Fang, Qing Li. Detection Method of Downpipe Diseases Based on Visual Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815001

Download Citation

Set citation alerts for the article

Tools

Set citation alerts for the article

Save the article for my favorites

Paper Information