TY - GEN
T1 - Efficient Lightweight Network with Transformer-Based Distillation for Micro-crack Detection of Solar Cells
AU - Xie, Xiangying
AU - Liu, Xinyue
AU - Chen, Qi Xiang
AU - Leng, Biao
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - Micro-cracks on solar cells often affect the power generation efficiency, so this paper proposes a lightweight network for cell image micro-crack detection task. Firstly, a Feature Selection framework is proposed, which can efficiently and adaptively decide the number of layers of the feature extraction network, and clip unnecessary feature generation process. In addition, based on the design of the Transformer layer, Transformer Distillation is proposed. In Transformer Distillation, the designed Transformer Refine module excavates the distillation information from the two dimensions of features and relations. Using a combination of Feature Selection and Transformer Distillation, the lightweight networks based on ResNet and ViT can achieve much better effects than the original networks, with classification accuracy rates of 88.58% and 89.35% respectively.
AB - Micro-cracks on solar cells often affect the power generation efficiency, so this paper proposes a lightweight network for cell image micro-crack detection task. Firstly, a Feature Selection framework is proposed, which can efficiently and adaptively decide the number of layers of the feature extraction network, and clip unnecessary feature generation process. In addition, based on the design of the Transformer layer, Transformer Distillation is proposed. In Transformer Distillation, the designed Transformer Refine module excavates the distillation information from the two dimensions of features and relations. Using a combination of Feature Selection and Transformer Distillation, the lightweight networks based on ResNet and ViT can achieve much better effects than the original networks, with classification accuracy rates of 88.58% and 89.35% respectively.
KW - Deep learning
KW - Defect detection
KW - Image classification
KW - Knowledge distillation
UR - https://www.scopus.com/pages/publications/85177857274
U2 - 10.1007/978-981-99-8067-3_1
DO - 10.1007/978-981-99-8067-3_1
M3 - 会议稿件
AN - SCOPUS:85177857274
SN - 9789819980666
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 15
BT - Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
A2 - Luo, Biao
A2 - Cheng, Long
A2 - Wu, Zheng-Guang
A2 - Li, Hongyi
A2 - Li, Chaojie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 30th International Conference on Neural Information Processing, ICONIP 2023
Y2 - 20 November 2023 through 23 November 2023
ER -