MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network

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.