SAFPN: a full semantic feature pyramid network for object detection

To enhance the performance of object detection algorithm, this paper proposes segmentation attention feature pyramid network (SAFPN) to address the issue of semantic information loss. Compared to prior works, SAFPN discards the original 1 x 1 convolutions and achieves feature dimension reduction through a segmentation and accumulation architecture, thereby preserving the semantic information of high-dimensional features completely. To capture fine-grained semantic details, it integrates channel attention and spatial attention mechanisms to enhance the network's focus on important information. Extensive experimental validation demonstrates that SAFPN achieves favorable results on multiple public datasets, and can better complete the target detection task.