Attention Pyramid Networks for Object Detection With Semantic Information Fusion

Abstract

Pyramid context information fusion has limitations in distinguishing background and important semantic information. The attention mechanism solves the limitations, but the detection effect for small target detection and occluded object detection is not good. In order to solve the above problems, a target detection method based on attention pyramid information fusion (APIFNet) is proposed. It consists of Dynamic Pyramid Attention Fusion Module (DPAF) and Attention Semantic Contextual Information Module (ACM). First, the DPAF module fuses information at different scales and simultaneously guides the learning of low-level features by high-level features to enhance semantic information and spatial details. Then, in order to detect small targets and occluded objects, the ACM module effectively enhances the detection ability of small targets by emphasizing the importance of foreground contextual semantic information and suppressing unimportant semantic information. Ultimately, APIFNet outperforms other methods in terms of evaluation performance on the COCO and PASCAL VOC data sets.