Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement

In recent years, object detection algorithm based on deep learning has made great progress, but the detection effect is not ideal for small objects detection. Some methods use high-resolution features or enhance shallow features to improve the detection accuracy of small objects. However, using high-resolution features for detection needs higher computational cost, and enhancing shallow features by propagating semantic information from high-level into low-level may bring information aliasing. To address this issue, we propose a novel object detection method based on shallow feature fusion and semantic information enhancement (FFSI). The high-level semantic information is injected into low-level features to guide the enhancement of specific detail information. In order to reduce the information aliasing in shallow features and enhance the receptive field of shallow features, we design two parallel modules: context information enhancement module (CIE) and receptive field enhancement module (RFE). CIE highlights the location of objects by establishing the relationship between local and global context information. RFE enhances the receptive field of shallow features by using dilated convolution to adapt to object detection of different scales, especially small objects. The proposed model is evaluated extensively on PASCAL VOC, and COCO datasets. The experimental results demonstrate that the proposed FFSI model has competitive performance. More importantly, this study reveals that FFSI outperforms the state-of-the-art methods in detecting small objects