Detecting Small Objects using Multi-Scale Feature Fusion Mechanism with Convolutional Block Attention

Small object detection is difficult because of their low resolution and the inclusion of unimportant background information. Aiming at the problem of small objects information loss in multiscale feature fusion and the impact of background information, this paper proposes a multi-scale feature fusion mechanism (MFFM) with the convolutional block attention modules (CBAM). The proposed approach efficiently utilizes the low three-layer feature information (P1-P3) output from the backbone network and the improved feature fusion technique to enhance the characterization ability of the single-layer feature information through the attention mechanism. This mechanism enables the single-layer features to carry the three-layer feature information, thereby improving the fusion ability among the feature layers. The experimental results demonstrate that MFFM enhances the overall accuracy mAP on the VisDrone2019-DET validation set by 1.4% and the accuracy APs on small objects by 1.5% in comparison to the baseline model YOLOX-X. This approach effectively improves the performance of small object detection.