TY - GEN
T1 - A Lightweight Tire Tread Image Classification Network
AU - Zhang, Fenglei
AU - Li, Da
AU - Li, Shenghua
AU - Guan, Weili
AU - Liu, Meng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - VCIP 2022 'Tire pattern image classification based on lightweight network challenge' aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on ImageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves supe-rior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.
AB - VCIP 2022 'Tire pattern image classification based on lightweight network challenge' aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on ImageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves supe-rior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.
KW - knowledge distillation
KW - lightweight image classification
KW - tire tread image classification
UR - https://www.scopus.com/pages/publications/85147250311
U2 - 10.1109/VCIP56404.2022.10008894
DO - 10.1109/VCIP56404.2022.10008894
M3 - 会议稿件
AN - SCOPUS:85147250311
T3 - 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
BT - 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
Y2 - 13 December 2022 through 16 December 2022
ER -