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A vehicle detection method based on disparity segmentation

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Abstract

The detection of small objects has always been one of the key challenges in vehicle detection. In this work, a standard for dividing the object more accurately than traditional methods is presented. Based on the division standard of disparity segmentation, we propose a novel multi-scale detection network aiming to reduce the transmission of redundant information between each scale. We divide the objects by depth, which is the distance from the object to the viewpoint. Then, a multi-branch architecture providing specialized detection for objects of each scale separately is constructed. Through ablation experiments, our method achieves an increase of 1.63 to 2.01 mAP compared with the baseline method. On the KITTI dataset, our method combined with state-of-arts achieves an increase of 3.54 mAP for small objects and 0.79 mAP for medium objects.

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References

  1. Adelson EH, Anderson CH, Bergen JR, Burt PJ, Ogden JM (1984) Pyramid methods in image processing. RCA engineer 29(6):33–41

    Google Scholar 

  2. Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS – improving object detection with one line of code. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp 5562–5570

  3. Cai Z, Fan Q, Feris RS (2016) A unified multi-scale deep convolutional neural network for fast object detection. European Conference on Computer Vision, Vasconcelos, N

  4. Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6154–6162

  5. Chen K, Wang J et al (2019) Open MMLab Detection Toolbox and Benchmark. arXiv:1906.07155

  6. Chen J, Xu W, Xu H, Lin F, Sun Y, Shi X (2018) Fast vehicle detection using a disparity projection method, IEEE Transactions on Intelligent Transportation Systems

  7. Choi HM, Kang H, Hyun Y (2019) Multi-view reprojection architecture for orientation estimation. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp 2357–2366

  8. Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. In: IEEE Access, vol 6, pp 19959–19967

  9. Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. Conference and Workshop on Neural Information Processing Systems

  10. Dai, Qi et al (2017) Deformable convolutional networks. Proceedings of the IEEE international conference on computer vision

  11. Duan K, Xie L, Qi H, Bai S, Huang Q, Tian Q (2020) Corner proposal network for anchor-free, two-stage object detection. Computer Vision - ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12348. Springer, Cham

  12. Fan S, Zhu F, Chen S et al (2021) FII-CenterNet: an anchor-free detector with foreground attention for traffic object detection. IEEE Transactions on Vehicular Technology

  13. Ghiasi G, Lin TY, Le QV (2019) NAS-FPN: learning scalable feature pyramid architecture for object detection. IEEE/CVF conference on computer vision and pattern recognition, pp 7029–7038

  14. Girshick R (2015) Fast R-CNN. IEEE International Conference on Computer Vision, pp 1440– 1448

  15. Girshick R, Donahue J, Darrell T, Malik J (2014) Fractional rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition, pp 580–587

  16. Guo L, Duan H, Zhou. W (2021) Multiple attention networks for stereo matching. Multimed Tools Appl 80:28583–28601

    Article  Google Scholar 

  17. He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. IEEE International Conference on Computer Vision, pp 2980–2988

  18. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  19. Hong S, Roh B, Kim KH et al (2016) PVANet: lightweight deep neural networks for real-time object detection. ArXiv:1611.08588: n. page

  20. Hu X et al (2017) SINet: a Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Trans Intell Transp Syst 20(3):1010–1019

    Article  Google Scholar 

  21. Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. IEEE/CVF International Conference on Computer Vision, pp 6053–6062

  22. Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. IEEE/CVF International Conference on Computer Vision, pp 6053–6062

  23. Li S, Yan Z, Li H et al (2021) Exploring intermediate representation for monocular vehicle pose estimation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

  24. Lin T-Y, Dollar P, Girshick RB, He K, Hariharan B, Belongie SJ (2017) Feature pyramid networks for object detection. IEEE Conference on Computer Vision and Pattern Recognition. pp 936–944

  25. Lin T-Y, Goyal P, Girshick R, He K, Dollar P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318–327

    Article  Google Scholar 

  26. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Berg, SSD: Single Shot MultiBox Detector. Springer, Cham

    Google Scholar 

  27. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. IEEE/CVF conference on computer vision and pattern recognition, pp 8759–8768

  28. Lu X, Wang W, Danelljan M, Zhou T, Shen J, Van Gool L (2020) Video object segmentation with episodic graph memory networks. Computer Vision - ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12348. Springer, Cham

  29. Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks, cvpr

  30. Lu X, Wang W, Shen J, Crandall D, Luo J (2019) Zero-shot video object segmentation with co-attention siamese networks. IEEE Trans Pattern Anal Mach Intell 44(4):2228–2242

    Google Scholar 

  31. Pang Y, Zhao X, Zhang L et al (2020) Multi-scale interactive network for salient object detection. IEEE/CVF conference on computer vision and pattern recognition, pp 9410–9419

  32. Ren J, Chen X, Liu J et al (2017) Accurate single stage detector using recurrent rolling convolution. IEEE conference on computer vision and pattern recognition, pp 752–760

  33. Redmon J, Divvala S, Girshick R, Farhadi A You only look once: Unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition, pp 779-788

  34. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  35. Singh B, Davis LS (2018) An analysis of scale invariance in object detection–SNIP. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3578-3587

  36. Singh B, Najibi M, Davis LS (2018) SNIPER: Efficient multi-scale training. Conference and Workshop on Neural Information Processing Systems

  37. Wu J, Liu S, Huang D et al (2020) Multi-scale positive sample refinement for few-shot object detection. ArXiv:2007.09384: n. pag

  38. Zhang Y, Chu J, Leng L, Miao J (2020) Mask-refined R-CNN: a network for refining object details in instance segmentation. Sensors (Basel, Switzerland)

  39. Zhao Q, Sheng T, Wang Y et al (2019) M2Det: a single-shot object detector based on multi-level feature pyramid network. The Association for the Advance of Artificial Intelligence

  40. Zhu R et al (2019) ScratchDet: training single-shot object detectors from scratch. In: IEEE/CVF conference on computer vision and pattern recognition, pp 2263–2272

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    Authors and Affiliations

    1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

      Shiyang Li, Jing Chen, Weimin Peng & Xiaoying Shi

    2. School of Mechanical Engineering, Tongji Univerity, Shanghai, 201804, China

      Wanghui Bu

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    1. Shiyang Li
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    2. Jing Chen
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    3. Weimin Peng
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    4. Xiaoying Shi
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    Correspondence to Jing Chen.

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    Jing Chen, Weimin Peng, Xiaoying Shi and Wanghui Bu are contributed equally to this work.

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    Cite this article

    Li, S., Chen, J., Peng, W. et al. A vehicle detection method based on disparity segmentation. Multimed Tools Appl 82, 19643–19655 (2023). https://doi.org/10.1007/s11042-023-14360-x

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