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Appl."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>\n            Detecting objects in aerial images is a long-standing and challenging problem since the objects in aerial images vary dramatically in size and orientation. Most existing neural network based methods are not robust enough to provide accurate oriented object detection results in aerial images since they do not consider the correlations between different levels and scales of features. In this paper, we propose a novel two-stage network-based detector with\n            <jats:bold>\n              <jats:underline>a<\/jats:underline>\n              daptive\n              <jats:underline>f<\/jats:underline>\n              eature\n              <jats:underline>f<\/jats:underline>\n              usion towards highly accurate oriented object\n              <jats:underline>det<\/jats:underline>\n              ection\n            <\/jats:bold>\n            in aerial images, named\n            <jats:bold>AFF-Det<\/jats:bold>\n            . First, a\n            <jats:bold>multi-scale feature fusion module (MSFF)<\/jats:bold>\n            is built on the top layer of the extracted feature pyramids to mitigate the semantic information loss in the small-scale features. We also propose a cascaded oriented bounding box regression method to transform the horizontal proposals into oriented ones. Then the transformed proposals are assigned to all\n            <jats:bold>feature pyramid network (FPN)<\/jats:bold>\n            levels and aggregated by the\n            <jats:bold>weighted RoI feature aggregation (WRFA)<\/jats:bold>\n            module. The above modules can adaptively enhance the feature representations in different stages of the network based on the attention mechanism. Finally, a rotated decoupled-RCNN head is introduced to obtain the classification and localization results. Extensive experiments are conducted on the DOTA and HRSC2016 datasets to demonstrate the advantages of our proposed AFF-Det. The best detection results can achieve 80.73% mAP and 90.48% mAP, respectively, on these two datasets, outperforming recent state-of-the-art methods.\n          <\/jats:p>","DOI":"10.1145\/3513133","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T19:42:08Z","timestamp":1645213328000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Towards Accurate Oriented Object Detection in Aerial Images with Adaptive Multi-level Feature Fusion"],"prefix":"10.1145","volume":"19","author":[{"given":"Peining","family":"Zhen","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Minhang District, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Minhang District, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Astronautical Systems Engineering, Fengtai District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotao","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Astronautical Systems Engineering, Fengtai District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Astronautical Systems Engineering, Fengtai District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Ji","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Minhang District, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai-Bao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Minhang District, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"150","volume-title":"Asian Conference on Computer Vision","author":"Azimi Seyed Majid","year":"2018","unstructured":"Seyed Majid Azimi, Eleonora Vig, Reza Bahmanyar, Marco K\u00f6rner, and Peter Reinartz. 2018. 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In Proceedings of the International Conference on Machine Learning, Vol. 139. 11830\u201311841."},{"key":"e_1_3_1_39_2","article-title":"SCRDet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing","author":"Yang Xue","year":"2020","unstructured":"Xue Yang, Junchi Yan, Xiaokang Yang, Jin Tang, Wenlong Liao, and Tao He. 2020. SCRDet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. arXiv preprint arXiv:2004.13316. (2020).","journal-title":"arXiv preprint arXiv:2004.13316."},{"key":"e_1_3_1_40_2","first-page":"8232","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Yang Xue","year":"2019","unstructured":"Xue Yang, Jirui Yang, Junchi Yan, Yue Zhang, Tengfei Zhang, Zhi Guo, Xian Sun, and Kun Fu. 2019. SCRDet: Towards more robust detection for small, cluttered and rotated objects. In Proceedings of the IEEE International Conference on Computer Vision. 8232\u20138241."},{"key":"e_1_3_1_41_2","article-title":"Learning high-precision bounding box for rotated object detection via Kullback-Leibler divergence","author":"Yang Xue","year":"2021","unstructured":"Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang, Qi Tian, and Junchi Yan. 2021. Learning high-precision bounding box for rotated object detection via Kullback-Leibler divergence. arXiv preprint arXiv:2106.01883. (2021).","journal-title":"arXiv preprint arXiv:2106.01883."},{"key":"e_1_3_1_42_2","unstructured":"Xue Yang Yue Zhou and Junchi Yan. 2021. AlphaRotate: A rotation detection benchmark using TensorFlow. (2021). https:\/\/github.com\/yangxue0827\/RotationDetection."},{"key":"e_1_3_1_43_2","first-page":"2150","volume-title":"IEEE Winter Conference on Applications of Computer Vision","author":"Yi Jingru","year":"2021","unstructured":"Jingru Yi, Pengxiang Wu, Bo Liu, Qiaoying Huang, Hui Qu, and Dimitris Metaxas. 2021. Oriented object detection in aerial images with box boundary-aware vectors. In IEEE Winter Conference on Applications of Computer Vision. 2150\u20132159."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2930982"},{"issue":"4","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"3518","DOI":"10.1109\/TGRS.2020.3018106","article-title":"GRS-Det: An anchor-free rotation ship detector based on Gaussian-mask in remote sensing images","volume":"59","author":"Zhang Xiangrong","year":"2021","unstructured":"Xiangrong Zhang, Guanchun Wang, Peng Zhu, Tianyang Zhang, Chen Li, and Licheng Jiao. 2021. 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