TY - JOUR
T1 - CAGNet
T2 - A Multi-Scale Convolutional Attention Method for Glass Detection Based on Transformer
AU - Hu, Xiaohang
AU - Gao, Rui
AU - Yang, Seungjun
AU - Cho, Kyungeun
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/10
Y1 - 2023/10
N2 - Glass plays a vital role in several fields, making its accurate detection crucial. Proper detection prevents misjudgments, reduces noise from reflections, and ensures optimal performance in other computer vision tasks. However, the prevalent usage of glass in daily applications poses unique challenges for computer vision. This study introduces a novel convolutional attention glass segmentation network (CAGNet) predicated on a transformer architecture customized for image glass detection. Based on the foundation of our prior study, CAGNet minimizes the number of training cycles and iterations, resulting in enhanced performance and efficiency. CAGNet is built upon the strategic design and integration of two types of convolutional attention mechanisms coupled with a transformer head applied for comprehensive feature analysis and fusion. To further augment segmentation precision, the network incorporates a custom edge-weighting scheme to optimize glass detection within images. Comparative studies and rigorous testing demonstrate that CAGNet outperforms several leading methodologies in glass detection, exhibiting robustness across a diverse range of conditions. Specifically, the IOU metric improves by 0.26% compared to that in our previous study and presents a 0.92% enhancement over those of other state-of-the-art methods.
AB - Glass plays a vital role in several fields, making its accurate detection crucial. Proper detection prevents misjudgments, reduces noise from reflections, and ensures optimal performance in other computer vision tasks. However, the prevalent usage of glass in daily applications poses unique challenges for computer vision. This study introduces a novel convolutional attention glass segmentation network (CAGNet) predicated on a transformer architecture customized for image glass detection. Based on the foundation of our prior study, CAGNet minimizes the number of training cycles and iterations, resulting in enhanced performance and efficiency. CAGNet is built upon the strategic design and integration of two types of convolutional attention mechanisms coupled with a transformer head applied for comprehensive feature analysis and fusion. To further augment segmentation precision, the network incorporates a custom edge-weighting scheme to optimize glass detection within images. Comparative studies and rigorous testing demonstrate that CAGNet outperforms several leading methodologies in glass detection, exhibiting robustness across a diverse range of conditions. Specifically, the IOU metric improves by 0.26% compared to that in our previous study and presents a 0.92% enhancement over those of other state-of-the-art methods.
KW - convolutional attention
KW - feature analyze
KW - semantic segmentation
KW - transformer
UR - http://www.scopus.com/inward/record.url?scp=85176546685&partnerID=8YFLogxK
U2 - 10.3390/math11194084
DO - 10.3390/math11194084
M3 - Article
AN - SCOPUS:85176546685
SN - 2227-7390
VL - 11
JO - Mathematics
JF - Mathematics
IS - 19
M1 - 4084
ER -