2019

Object Detection in 20 Years: A Survey

Zou, Zhengxia, Chen, Keyan, Shi, Zhenwei et al.

Understand

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.

Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field.
If we consider today's object detection technique as a revolution driven by deep learning, then back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision.
This paper extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022).

Built on

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Y. Chen, T. Yang, X. Zhang, G. Meng, C. Pan, and J. Sun, “Detnas: Neural architecture search on object detection,” arXiv preprint arXiv:1903.10979 , 2019
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2019
Closest in time.
H. Xu, L. Yao, W. Zhang, X. Liang, and Z. Li, “Auto-fpn: Automatic network architecture adaptation for object detection beyond classification,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6649–6658
2019
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G. Ghiasi, T.-Y. Lin, and Q. V. Le, “Nas-fpn: Learning scalable feature pyramid architecture for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7036–7045
2019
Closest in time.
J. Ding, N. Xue, Y. Long, G.-S. Xia, and Q. Lu, “Learning roi transformer for oriented object detection in aerial images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2849–2858
2019
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D. Zhang, J. Han, L. Zhao, and D. Meng, “Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework,” International Journal of Computer Vision , vol. 127, no. 4, pp. 363–380, 2019
2019
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X. Zhu, J. Pang, C. Yang, J. Shi, and D. Lin, “Adapting object detectors via selective cross-domain alignment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 687–696
2019
Closest in time.
K. Saito, Y. Ushiku, T. Harada, and K. Saenko, “Strong-weak distribution alignment for adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6956–6965
2019
Closest in time.
T. Kim, M. Jeong, S. Kim, S. Choi, and C. Kim, “Diversify and match: A domain adaptive representation learning paradigm for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 456–12 465
2019
Closest in time.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934 , 2020
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2020
Closest in time.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213–229
2020
Closest in time.
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” arXiv preprint arXiv:2010.04159 , 2020
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2020
Closest in time.
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, T. Duerig, and V. Ferrari, “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” IJCV , 2020
2020
Closest in time.
L. V. Pato, R. Negrinho, and P. M. Q. Aguiar, “Seeing without looking: Contextual rescoring of object detections for ap maximization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Closest in time.
Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-iou loss: Faster and better learning for bounding box regression,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 993–13 000
2020
Closest in time.
J. Guo, K. Han, Y. Wang, C. Zhang, Z. Yang, H. Wu, X. Chen, and C. Xu, “Hit-detector: Hierarchical trinity architecture search for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 405–11 414
2020
Closest in time.
N. Wang, Y. Gao, H. Chen, P. Wang, Z. Tian, C. Shen, and Y. Zhang, “Nas-fcos: Fast neural architecture search for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 943–11 951
2020
Closest in time.
L. Yao, H. Xu, W. Zhang, X. Liang, and Z. Li, “Sm-nas: structural-to-modular neural architecture search for object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 661–12 668
2020
Closest in time.
C. Jiang, H. Xu, W. Zhang, X. Liang, and Z. Li, “Sp-nas: Serial-to-parallel backbone search for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 863–11 872
2020
Closest in time.
C.-Y. Wang, H.-Y. M. Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, “Cspnet: A new backbone that can enhance learning capability of cnn,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , 2020, pp. 390–391
2020
Closest in time.
D. Zhang, W. Zeng, J. Yao, and J. Han, “Weakly supervised object detection using proposal-and semantic-level relationships,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Closest in time.
C.-D. Xu, X.-R. Zhao, X. Jin, and X.-S. Wei, “Exploring categorical regularization for domain adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 724–11 733
2020
Closest in time.
H.-K. Hsu, C.-H. Yao, Y.-H. Tsai, W.-C. Hung, H.-Y. Tseng, M. Singh, and M.-H. Yang, “Progressive domain adaptation for object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 749–757
2020
Closest in time.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” arXiv preprint arXiv:2103.14030 , 2021
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2021
Closest in time.
R. Solovyev, W. Wang, and T. Gabruseva, “Weighted boxes fusion: Ensembling boxes from different object detection models,” Image and Vision Computing , vol. 107, p. 104117, 2021
2021
Closest in time.
Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu, and W. Zuo, “Enhancing geometric factors in model learning and inference for object detection and instance segmentation,” IEEE Transactions on Cybernetics , 2021
2021
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J. Wang, L. Song, Z. Li, H. Sun, J. Sun, and N. Zheng, “End-to-end object detection with fully convolutional network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 849–15 858
2021
Closest in time.
G. Yu, Q. Chang, W. Lv, C. Xu, C. Cui, W. Ji, Q. Dang, K. Deng, G. Wang, Y. Du et al. , “Pp-picodet: A better real-time object detector on mobile devices,” arXiv preprint arXiv:2111.00902 , 2021
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2021
Closest in time.
D. Zhang, J. Han, G. Cheng, and M.-H. Yang, “Weakly supervised object localization and detection: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 9, pp. 5866–5885, 2021
2021
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Y. Wang, R. Zhang, S. Zhang, M. Li, Y. Xia, X. Zhang, and S. Liu, “Domain-specific suppression for adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9603–9612
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L. Hou, Y. Zhang, K. Fu, and J. Li, “Informative and consistent correspondence mining for cross-domain weakly supervised object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9929–9938
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P. Sun, Y. Jiang, E. Xie, W. Shao, Z. Yuan, C. Wang, and P. Luo, “What makes for end-to-end object detection?” in International Conference on Machine Learning . PMLR, 2021, pp. 9934–9944
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X. Zhou, X. Xu, W. Liang, Z. Zeng, S. Shimizu, L. T. Yang, and Q. Jin, “Intelligent small object detection for digital twin in smart manufacturing with industrial cyber-physical systems,” IEEE Transactions on Industrial Informatics , vol. 18, no. 2, pp. 1377–1386, 2021
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C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv preprint arXiv:2207.02696 , 2022
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2022
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C. Yang, Z. Huang, and N. Wang, “Querydet: Cascaded sparse query for accelerating high-resolution small object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 668–13 677
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G. Cheng, X. Yuan, X. Yao, K. Yan, Q. Zeng, and J. Han, “Towards large-scale small object detection: Survey and benchmarks,” arXiv preprint arXiv:2207.14096 , 2022
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2022
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Y. Wang, V. C. Guizilini, T. Zhang, Y. Wang, H. Zhao, and J. Solomon, “Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,” in Conference on Robot Learning . PMLR, 2022, pp. 180–191
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Y. Wang, T. Ye, L. Cao, W. Huang, F. Sun, F. He, and D. Tao, “Bridged transformer for vision and point cloud 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 114–12 123
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Closest in time.
X. Cheng, H. Xiong, D.-P. Fan, Y. Zhong, M. Harandi, T. Drummond, and Z. Ge, “Implicit motion handling for video camouflaged object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 864–13 873
2022
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Q. Zhou, X. Li, L. He, Y. Yang, G. Cheng, Y. Tong, L. Ma, and D. Tao, “Transvod: End-to-end video object detection with spatial-temporal transformers,” arXiv preprint arXiv:2201.05047 , 2022
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2022
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R. Cong, Q. Lin, C. Zhang, C. Li, X. Cao, Q. Huang, and Y. Zhao, “Cir-net: Cross-modality interaction and refinement for rgb-d salient object detection,” IEEE Transactions on Image Processing , 2022
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Y. Wang, L. Zhu, S. Huang, T. Hui, X. Li, F. Wang, and S. Liu, “Cross-modality domain adaptation for freespace detection: A simple yet effective baseline,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 4031–4042
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C. Feng, Y. Zhong, Z. Jie, X. Chu, H. Ren, X. Wei, W. Xie, and L. Ma, “Promptdet: Expand your detector vocabulary with uncurated images,” arXiv preprint arXiv:2203.16513 , 2022
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Y. Zhong, J. Yang, P. Zhang, C. Li, N. Codella, L. H. Li, L. Zhou, X. Dai, L. Yuan, Y. Li et al. , “Regionclip: Region-based language-image pretraining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 793–16 803
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M. Jaderberg, K. Simonyan, A. Zisserman et al. , “Spatial transformer networks,” in Advances in neural information processing systems , 2015, pp. 2017–2025
2025
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K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in ICML , 2015, pp. 2048–2057
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