Multi-scale oriented object detection based on improved RoI Transformer in remote sensing images
Keywords:
high-resolution networkAbstract
Oriented object detection is a crucial task in remote sensing image processing. The large-scale variations and arbitrary orientations of objects bring challenges to automatic object detection. An improved RoI Transformer detection framework was proposed to address above-mentioned problems. Firstly, RoI Transformer detection framework was used to obtain rotated region of interest (RRoI) for extraction of robust geometric features. Secondly, high-resolution network (HRNet) was introduced in the detector to extract multiresolution feature maps, which could maintain high-resolution features while adapting to multi-scale changes of the target. Finally, Kullback-Leibler divergence (KLD) loss was introduced to solve angle periodicity problem caused by the standard representation of oriented object, and improve the adaptability of RoI Transformer to targets in arbitrary directions. The object localization accuracy was also improved through the joint optimization of bounding box parameters of oriented object. The proposed method, called HRD-ROI Transformer (HRNet+KLD ROI Transformer), was compared with the typical oriented object detection method on two public datasets, namely DOTAv1.0 and DIOR-R. The results show that the mean-average-precision (mAP) of detection results on DOTAv1.0 and DIOR-R datasets is improved by 3.7% and 4%, respectively. © 2023 Editorial office of Journal of Applied Optics. All rights reserved.
References
LIU L, OUYANG W, WANG X G, Et al., Deep learning for generic object detection: a survey[J], International Journal of Computer Vision, 128, 2, pp. 261-318, (2020); FU Changhong, CHEN Kunhui, LU Kunhan, Et al., Aviation fastener rotation detection for intelligent optical perception with edge computing[J], Journal of Applied Optics, 43, 3, pp. 472-480, (2022); DING J, XUE N, LONG Y, Et al., Learning RoI transformer for oriented object detection in aerial images, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2849-2858, (2019); QIAN W, YANG X, PENG S L, Et al., Learning modulated loss for rotated object detection, Proceedings of the AAAI conference on artificial intelligence, pp. 2458-2466, (2021); MA J Q, SHAO W Y, YE H, Et al., Arbitrary-oriented scene text detection via rotation proposals[J], IEEE Transactions on Multimedia, 20, 11, pp. 3111-3122, (2018); XIE X X, CHENG G, WANG J B, Et al., Oriented r-cnn for object detection, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3520-3529, (2021); HAN J M, DING J, XUE N, Et al., Redet: a rotationequivariant detector for aerial object detection, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2786-2795, (2021); HE K M, ZHANG X Y, REN S Q, Et al., Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016); YANG X, YAN J C, MING Q, Et al., Rethinking rotated object detection with Gaussian Wasserstein distance loss, International Conference on Machine Learning, pp. 11830-11841, (2021); YU Y, DA F P., Phase-shifting coder: predicting accurate orientation in oriented object detection; YANG X, YAN J C, FENG Z M, Et al., R3det: refined single-stage detector with feature refinement for rotating object [C], Proceedings of the AAAI conference on artificial intelligence, pp. 3163-3171, (2021); HOU L, LU K, XUE J, Et al., Shape-adaptive selection and measurement for oriented object detection, Proceedings of the AAAI Conference on Artificial Intelligence, pp. 923-932, (2022); LI W, CHEN Y, HU K, Et al., Oriented reppoints for aerial object detection, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1829-1838, (2022); WU Liequan, ZHOU Zhifeng, ZHU Zhiling, Et al., Surface defect detection of patch diode based on improved YOLO-V4[J], Journal of Applied Optics, 44, 3, pp. 621-627, (2023); YANG X, YANG X J, YANG J R, Et al., Learning high-precision bounding box for rotated object detection via kullback leibler divergence, Advances in Neural Information Processing Systems, pp. 18381-18394, (2021); YANG X, ZHOU Y, ZHANG G F, Et al., The kfiou loss for rotated object detection; WANG K, LI Z, SU A, Et al., Oriented object detection in optical remote sensing images: a survey; WANG J D, SUN K, CHENG T S, Et al., Deep high-resolution representation learning for visual recognition[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 10, pp. 3349-3364, (2021); CAO Jiale, LI Yali, SUN Hanqing, Et al., A survey on deep learning based visual object detection[J], Journal of Image and Graphics, 27, 6, pp. 1697-1722, (2022); YANG X, YAN J C, LIAO W L, Et al., SCRDet++: detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 2, pp. 2384-2399, (2023); HAN J, DING J, LI J, Et al., Align deep features for oriented object detection[J], IEEE Transactions on Geoscience and Remote Sensing, 60, pp. 1-11, (2022); XIA G S, BAI X, DING J, Et al., Dota: a large-scale dataset for object detection in aerial images, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3974-3983, (2018); CHENG G, WANG J B, LI K, Et al., Anchor-free oriented proposal generator for object detection[J], IEEE Transactions on Geoscience and Remote Sensing, 60, pp. 1-11, (2022); LI K, WAN G, CHENG G, Et al., Object detection in optical remote sensing images: a survey and a new benchmark[J], ISPRS Journal of Photogrammetry and Remote Sensing, 159, pp. 296-307, (2020); ZHOU Y, YANG X, ZHANG G F, Et al., Mmrotate: a rotated object detection benchmark using pytorch, Proceedings of the 30th ACM International Conference on Multimedia, pp. 7331-7334, (2022); WU Y X, KIRILLOV A, MASSA F, Et al., Detectron2; WANG R S, DUAN Y F, LI Y K.,
Segmenting anything also detect anything; LI J, GONG Y X, MA Z, Et al., Enhancing feature fusion using attention for small object detection, 2022 IEEE 8th International Conference on Computer and Communications, pp. 1859-1863, (2022);
YUAN Y, ZHANG Y L., OLCN:
an optimized low coupling network for small objects detection[J], IEEE Geoscience and Remote Sensing Letters, 19, pp. 1-5, (2021)
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