TY - GEN
T1 - Performance Study of CBAM Attention Mechanism in Convolutional Neural Networks at Different Depths
AU - Yang, Chunling
AU - Zhang, Chunchao
AU - Yang, Xuqiang
AU - Li, Yanbin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Compared with traditional imaging methods, infrared imaging has the advantages of strong anti-interference and good concealment, and is widely used in the fields of infrared alarm and reconnaissance. The target detection algorithm based on deep learning is much better than the traditional algorithm in target detection performance, but its small target detection performance is poor. The performance of small target detection can be improved by introducing attention mechanism. This paper aims to study the impact of adding Convolutional Block Attention Module (CBAM) to Convolutional Neural Network (CNN) on its performance, especially in small target detection. By adding CBAM at different depths, this paper explores the contribution of CBAM structure at different depths to the detection performance of small targets in convolutional neural networks. The results show that the small target detection performance of convolutional neural networks is significantly improved with CBAM attention mechanism, and the contribution of the performance on small target detection tasks varies by adding CBAM at different convolutional neural network structures depth.
AB - Compared with traditional imaging methods, infrared imaging has the advantages of strong anti-interference and good concealment, and is widely used in the fields of infrared alarm and reconnaissance. The target detection algorithm based on deep learning is much better than the traditional algorithm in target detection performance, but its small target detection performance is poor. The performance of small target detection can be improved by introducing attention mechanism. This paper aims to study the impact of adding Convolutional Block Attention Module (CBAM) to Convolutional Neural Network (CNN) on its performance, especially in small target detection. By adding CBAM at different depths, this paper explores the contribution of CBAM structure at different depths to the detection performance of small targets in convolutional neural networks. The results show that the small target detection performance of convolutional neural networks is significantly improved with CBAM attention mechanism, and the contribution of the performance on small target detection tasks varies by adding CBAM at different convolutional neural network structures depth.
KW - Convolutional Block Attention Module
KW - Convolutional Neural Network
KW - Infrared Small Target
KW - Target Detection
UR - https://www.scopus.com/pages/publications/85173616914
U2 - 10.1109/ICIEA58696.2023.10241832
DO - 10.1109/ICIEA58696.2023.10241832
M3 - 会议稿件
AN - SCOPUS:85173616914
T3 - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
SP - 1373
EP - 1377
BT - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
A2 - Cai, Wenjian
A2 - Yang, Guilin
A2 - Qiu, Jun
A2 - Gao, Tingting
A2 - Jiang, Lijun
A2 - Zheng, Tianjiang
A2 - Wang, Xinli
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Y2 - 18 August 2023 through 22 August 2023
ER -