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Recently, methods based on Faster region-based convolutional neural network (R-CNN) have been popular in multi-class object detection in remote sensing images due to their outstanding detection performance. The methods generally propose candidate region of interests (ROIs) through a region propose network (RPN), and the regions with high enough intersection-over-union (IoU) values against ground truth are treated as positive samples for training. In this paper, we find that the detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially, detection performance of small objects is poor when choosing a normal higher threshold, while a lower threshold will result in poor location accuracy caused by a large quantity of false positives. To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework for multi-class object detection. Specially, by analyzing the different roles that IoU can play in different parts of the network, we propose an IoU-guided detection framework to reduce the loss of small object information during training. Besides, the IoU-based weighted loss is designed, which can learn the IoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspect ratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves the precision of the results. Extensive experiments validate the effectiveness of our approach and we achieve state-of-the-art detection performance on the DOTA dataset.

IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery