Affiliation:
1. School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
2. School of Mathematics and Statistics, Henan University, Kaifeng 475001, China
Abstract
Although many state-of-the-art object detectors have been developed, detecting small and densely packed objects with complicated orientations in remote sensing aerial images remains challenging. For object detection in remote sensing aerial images, different scales, sizes, appearances, and orientations of objects from different categories could most likely enlarge the variance in the detection error. Undoubtedly, the variance in the detection error should have a non-negligible impact on the detection performance. Motivated by the above consideration, in this paper, we tackled this issue, so that we could improve the detection performance and reduce the impact of this variance on the detection performance as much as possible. By proposing a scaled smooth L1 loss function, we developed a new two-stage object detector for remote sensing aerial images, named Faster R-CNN-NeXt with RoI-Transformer. The proposed scaled smooth L1 loss function is used for bounding box regression and makes regression invariant to scale. This property ensures that the bounding box regression is more reliable in detecting small and densely packed objects with complicated orientations and backgrounds, leading to improved detection performance. To learn rotated bounding boxes and produce more accurate object locations, a RoI-Transformer module is employed. This is necessary because horizontal bounding boxes are inadequate for aerial image detection. The ResNeXt backbone is also adopted for the proposed object detector. Experimental results on two popular datasets, DOTA and HRSC2016, show that the variance in the detection error significantly affects detection performance. The proposed object detector is effective and robust, with the optimal scale factor for the scaled smooth L1 loss function being around 2.0. Compared to other promising two-stage oriented methods, our method achieves a mAP of 70.82 on DOTA, with an improvement of at least 1.26 and up to 16.49. On HRSC2016, our method achieves an mAP of 87.1, with an improvement of at least 0.9 and up to 1.4.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference72 articles.
1. Lim, J., Astrid, M., Yoon, H., and Lee, S. (2021, January 13–16). Small object detection using context and attention. Proceedings of the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Republic of Korea.
2. Detection of Cars in High-Resolution Aerial images of Complex Urban Environments;EIMikaty;IEEE Trans. Geosci. Remote Sens.,2017
3. Feature extraction by rotation-invariant matrix representation for object detection in aerial image;Wang;IEEE Geosci. Remote Sens. Lett.,2017
4. Cheng, G., Zhou, P., and Han, J. (2016, January 27–30). RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.
5. Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks;Deng;J-STARS,2017
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献