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 outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha 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 thatthe 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 alower 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 formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.
Effective Complex Airport Object Detection in Remote Sensing Images Based on Improved End-to-End Convolutional Neural Network
