TY - GEN
T1 - Application of an Improved Residual Attention Neural Network in Mechanical Part Classification
AU - Zhang, Tianrui
AU - Wang, Aizeng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This paper introduces an improved neural network that integrates residual network and attention mechanisms, achieving a significant improvement of 99.50% accuracy in mechanical part classification tasks. The proposed network enhance accuracy, precision, recall, and F1-score compared to traditional CNNs and other advanced networks in handling complex backgrounds and multi-angle part images. This advancement shows potential in smart manufacturing, offering greater automation, minimizing human intervention.
AB - This paper introduces an improved neural network that integrates residual network and attention mechanisms, achieving a significant improvement of 99.50% accuracy in mechanical part classification tasks. The proposed network enhance accuracy, precision, recall, and F1-score compared to traditional CNNs and other advanced networks in handling complex backgrounds and multi-angle part images. This advancement shows potential in smart manufacturing, offering greater automation, minimizing human intervention.
KW - Attention Mechanism
KW - Mechanical Part Classification
KW - Residual Networks
KW - Smart Manufacturing
UR - https://www.scopus.com/pages/publications/105001257226
U2 - 10.1007/978-981-96-2914-5_30
DO - 10.1007/978-981-96-2914-5_30
M3 - 会议稿件
AN - SCOPUS:105001257226
SN - 9789819629138
T3 - Communications in Computer and Information Science
SP - 334
EP - 341
BT - Artificial Intelligence and Robotics - 9th International Symposium, ISAIR 2024, Revised Selected Papers
A2 - Lu, Huimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024
Y2 - 27 September 2024 through 30 September 2024
ER -