MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network
Issued Date
2024-01-01
Resource Type
ISSN
15462218
eISSN
15462226
Scopus ID
2-s2.0-85210757783
Journal Title
Computers, Materials and Continua
Volume
81
Issue
2
Start Page
2243
End Page
2259
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers, Materials and Continua Vol.81 No.2 (2024) , 2243-2259
Suggested Citation
Bhanbhro H., Hooi Y.K., Bin Zakaria M.N., Kusakunniran W., Amur Z.H. MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network. Computers, Materials and Continua Vol.81 No.2 (2024) , 2243-2259. 2259. doi:10.32604/cmc.2024.052138 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/102317
Title
MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Object detection has made a significant leap forward in recent years. However, the detection of small objects continues to be a great difficulty for various reasons, such as they have a very small size and they are susceptible to missed detection due to background noise. Additionally, small object information is affected due to the downsampling operations. Deep learning-based detection methods have been utilized to address the challenge posed by small objects. In this work, we propose a novel method, the Multi-Convolutional Block Attention Network (MCBAN), to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling process. The multi-convolutional attention block (MCAB); channel attention and spatial attention module (SAM) that make up MCAB, have been crafted to accomplish small object detection with higher precision. We have carried out the experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) and Pattern Analysis, Statical Modeling and Computational Learning (PASCAL) Visual Object Classes (VOC) datasets and have followed a step-wise process to analyze the results. These experiment results demonstrate that significant gains in performance are achieved, such as 97.75% for KITTI and 88.97% for PASCAL VOC. The findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.