QEDetr: DETR with Query Enhancement for Fine-Grained Object Detection - Chenguang Dong; Shan Jiang; Haijiang Sun; Jiang Li; Zhenglei Yu; Jiasong Wang; Jiacheng Wang (Remote Sensing)
Fine-grained object detection aims to accurately localize the object bounding box while identifying the specific model of the object, which is more challenging than conventional remote sensing object detection. Transformer-based object detector (DETR) can capture remote inter-feature dependencies by using attention, which is suitable for fine-grained object detection tasks. However, most existing DETR-like object detectors are not specifically optimized for remote sensing detection tasks. Therefore, we propose an oriented fine-grained object detection method based on transformers. First, we combine denoising training and angle coding to propose a baseline DETR-like object detector for oriented object detection. Next, we propose a new attention mechanism for extracting finer-grained features by constraining the angle of sampling points during the attentional process, ensuring that the sampling points are more evenly distributed across the object features. Then, we propose a multiscale fusion method based on bilinear pooling to obtain the enhanced query and initialize a more accurate object bounding box. Finally, we combine the localization accuracy of each query with its classification accuracy and propose a new classification loss to further enhance the high-quality queries. Evaluation results on the FAIR1M dataset show that our method achieves an average accuracy of 48.5856 mAP and the highest accuracy of 49.7352 mAP in object detection, outperforming other methods.