  Due to the effective performance of multi-scale feature fusion, Path
Aggregation FPN (PAFPN) is widely employed in YOLO detectors. However, it
cannot efficiently and adaptively integrate high-level semantic information
with low-level spatial information simultaneously. We propose a new model named
MAF-YOLO in this paper, which is a novel object detection framework with a
versatile neck named Multi-Branch Auxiliary FPN (MAFPN). Within MAFPN, the
Superficial Assisted Fusion (SAF) module is designed to combine the output of
the backbone with the neck, preserving an optimal level of shallow information
to facilitate subsequent learning. Meanwhile, the Advanced Assisted Fusion
(AAF) module deeply embedded within the neck conveys a more diverse range of
gradient information to the output layer.
  Furthermore, our proposed Re-parameterized Heterogeneous Efficient Layer
Aggregation Network (RepHELAN) module ensures that both the overall model
architecture and convolutional design embrace the utilization of heterogeneous
large convolution kernels. Therefore, this guarantees the preservation of
information related to small targets while simultaneously achieving the
multi-scale receptive field. Finally, taking the nano version of MAF-YOLO for
example, it can achieve 42.4% AP on COCO with only 3.76M learnable parameters
and 10.51G FLOPs, and approximately outperforms YOLOv8n by about 5.1%. The
source code of this work is available at:
https://github.com/yang-0201/MAF-YOLO.
