MBAN: multi-branch attention network for small object detection

Abstract

Recent years small object detection has seen remarkable advancement. However, small objects are difficult to accurately detect in complex scenes due to their low resolution. The downsampling operation inevitably leads to the loss of information for small objects. In order to solve these issues, this article proposes a novel Multi-branch Attention Network (MBAN) to improve the detection performance of small objects. Firstly, an innovative Multi-branch Attention Module (MBAM) is proposed, which consists of two parts, i.e. Multi-branch structure consisting of convolution and maxpooling, and the parameter-free SimAM attention mechanism. By combining these two parts, the number of network parameters is reduced, the information loss of small objects is reduced, and the representation of small object features is enhanced. Furthermore, to systematically solve the problem of small object localization, a pre-processing method called Adaptive Clustering Relocation (ACR) is proposed. To validate our network, we conducted extensive experiments on two benchmark datasets, i.e. NWPU VHR-10 and PASCAL VOC. The findings from the experiment demonstrates the significant performance gains of MBAN over most existing algorithms, the mAP of MBAN achieved 96.55% and 84.96% on NWPU VHR-10 and PASCAL VOC datasets, respectively, which proves that MBAN has significant performance in small object detection.