TY - JOUR
T1 - Vision based pixel-level bridge structural damage detection using a link ASPP network
AU - Deng, Wenlong
AU - Mou, Yongli
AU - Kashiwa, Takahiro
AU - Escalera, Sergio
AU - Nagai, Kohei
AU - Nakayama, Kotaro
AU - Matsuo, Yutaka
AU - Prendinger, Helmut
N1 - Funding Information:
This work was partially supported by (i) the NII International Internship Program, (ii) a grant on ?Research on improving predictability by blending deep learning and symbol processing? (Kakenhi no.: 16H06562) provided to the Graduate School of Engineering at The University of Tokyo, and (iii) the Spanish project TIN2016-74946-P (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research. This work is also partially supported by ICREA under the ICREA Academia programme.
Funding Information:
This work was partially supported by (i) the NII International Internship Program, (ii) a grant on “Research on improving predictability by blending deep learning and symbol processing” ( Kakenhi no.: 16H06562 ) provided to the Graduate School of Engineering at The University of Tokyo, and (iii) the Spanish project TIN2016-74946-P ( MINECO /FEDER, UE) and CERCA Programme/ Generalitat de Catalunya . We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research. This work is also partially supported by ICREA under the ICREA Academia programme.
Publisher Copyright:
© 2019
PY - 2020/2
Y1 - 2020/2
N2 - Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks.
AB - Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks.
KW - Deep learning
KW - Semantic image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85075322216&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2019.102973
DO - 10.1016/j.autcon.2019.102973
M3 - Journal article
AN - SCOPUS:85075322216
SN - 0926-5805
VL - 110
JO - Automation in Construction
JF - Automation in Construction
M1 - 102973
ER -