  Fine-grained object detection (FGOD) extends object detection with the
capability of fine-grained recognition. In recent two-stage FGOD methods, the
region proposal serves as a crucial link between detection and fine-grained
recognition. However, current methods overlook that some proposal-related
procedures inherited from general detection are not equally suitable for FGOD,
limiting the multi-task learning from generation, representation, to
utilization. In this paper, we present PETDet (Proposal Enhancement for
Two-stage fine-grained object detection) to better handle the sub-tasks in
two-stage FGOD methods. Firstly, an anchor-free Quality Oriented Proposal
Network (QOPN) is proposed with dynamic label assignment and attention-based
decomposition to generate high-quality oriented proposals. Additionally, we
present a Bilinear Channel Fusion Network (BCFN) to extract independent and
discriminative features of the proposals. Furthermore, we design a novel
Adaptive Recognition Loss (ARL) which offers guidance for the R-CNN head to
focus on high-quality proposals. Extensive experiments validate the
effectiveness of PETDet. Quantitative analysis reveals that PETDet with
ResNet50 reaches state-of-the-art performance on various FGOD datasets,
including FAIR1M-v1.0 (42.96 AP), FAIR1M-v2.0 (48.81 AP), MAR20 (85.91 AP) and
ShipRSImageNet (74.90 AP). The proposed method also achieves superior
compatibility between accuracy and inference speed. Our code and models will be
released at https://github.com/canoe-Z/PETDet.
