Open Access
Issue |
JNWPU
Volume 41, Number 6, Decembre 2023
|
|
---|---|---|
Page(s) | 1190 - 1197 | |
DOI | https://doi.org/10.1051/jnwpu/20234161190 | |
Published online | 26 February 2024 |
- LI Kequan, CHEN Yan, LIU Jiachen, et al. Survey of deep learning-based object detection algorithms[J]. Computer Engineering, 2022, 48(7): 1–12 (in Chinese) [Google Scholar]
- DONG Wenxuan, LIANG Hongtao, LIU Guozhu, et al. Review of deep convolution applied to target detection algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 1025–1042 (in Chinese) [Google Scholar]
- VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//31st International Conference on Neural Information Processing Systems, New York, 2017: 6000–6010 [Google Scholar]
- KIRILLOV A, USUNIER N, CARION N, et al. End-to-end object detection with transformers[C]//2020 European Conference on Computer Vision, Cham, 2020: 213–229 [Google Scholar]
- ZHOU Quan, NI Yinghao, MO Yuwei, et al. FMA-DETR: a Transformer object detection method without encoder[J/OL]. (2023-10-16)[2023-11-30]. [Article] (in Chinese) [Google Scholar]
- LIAO Junshuang, TAN Qinghong. DETR with multi-granularity spatial attention and spatial prior supervision[J/OL]. (2023-09-26)[2023-11-30]. [Article] (in Chinese) [Google Scholar]
- YAO Z, AI J, LI B, et al. Efficient DETR: improving end-to-end object detector with dense prior[J]. (2021-08-03)[2023-01-09]. [Article] [Google Scholar]
- DUAN K, BAI S, XIE L, et al. CenterNet: keypoint triplets for object detection[C]//2019 IEEE/CVF International Confer-ence on Computer Vision, Piscataway, 2019: 6568–6577 [Google Scholar]
- ZHU X, SU W, LU L, et al. Deformable DETR: deformable transformers for end-to-end object detection[C]//International Conference on Learning Representations, Montreal, 2020 [Google Scholar]
- LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]//13th European Conference on Computer Vision, Piscataway, 2014: 740–755 [Google Scholar]
- REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. [Article] [CrossRef] [Google Scholar]
- ZHU Zhangli, RAO Yuan, WU Yuan, et al. Research progress of attention mechanism in deep learning[J]. Journal of Chinese Information Processing, 2019, 33(6): 1–11 (in Chinese) [Google Scholar]
- GAO P, ZHENG M, WANG X, et al. Fast convergence of DETR with spatially modulated co-attention[C]//2021 International Conference on Computer Vision, Piscataway, 2021: 3601–3610 [Google Scholar]
- LIU Qingwen. Construction of vectorized HD map based on transformer[D]. Shenyang: Liaoning University, 2023 (in Chinese) [Google Scholar]
- LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318–327 [CrossRef] [Google Scholar]
- ZHOU D, FANG J, SONG X, et al. IoU loss for 2D/3D object detection[C]//2019 International Conference on 3D Vision, Piscataway, 2019: 85–94 [Google Scholar]
- GEIGER A, LENZ P, URTASUN R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, 2012: 3354–3361 [Google Scholar]
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