PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection

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 multitask learning from generation, representation, to utilization. In this article, we present a proposal enhancement for two-stage FGOD (PETDet) to better handle the subtasks in two-stage FGOD methods. First, an anchor-free quality-oriented proposal network (QOPN) is proposed with dynamic label assignment and attention-based decomposition to generate high-quality-oriented proposals. In addition, we present a bilinear channel fusion network (BCFN) to extract independent and discriminative features of the proposals. Furthermore, we designed a novel adaptive recognition loss (ARL) that offers guidance for the region-based convolutional neural networks (R-CNNs) 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 .