Skip to main content

Advertisement

SpringerLink
Log in

Object detection method based on global feature augmentation and adaptive regression in IoT

  • S.I. : SPIoT 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The intelligent processing and utilization of visual perception information is the key technology of Internet of Things (IoT), and object detection based on deep learning is of great significance for improving the intelligence and security of IoT. Due to the influence of factors such as changes in the state of object in actual scene, occlusion and background changes, object detection method of deep learning still has following problems: features extracted by backbone network are noisy and not representative, the positive and negative samples are not balanced, and labeled object is inaccurate due to occlusion. Therefore, this paper proposes an object detection method based on global feature augmentation and adaptive regression. HRFPN extracts more representative high-resolution features and performs global augmentation, which can effectively distinguish feature differences between object and background. In training phase, uniform sampling is introduced to mine hard samples, and the positive and negative samples in RPN phase are balanced to improve detection performance, and adaptive bounding box regression loss is proposed to reduce the influence of object occlusion and boundary blur. Experimental results on PASCAL VOC2007 and MS COCO2017 datasets show that the proposed detection method is superior to the latest methods such as Cascade RCNN, CornerNet and Mask RCNN, which has better robustness and accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Reference

  1. Cai Z, Fan Q, R S Feris et al (2016) A unifified multi-scale deep convolutional neural network for fast object detection. In: European conference on computer vision, pp 354–370

  2. Hu ZB, Xu XL, Su QH et al (2019) Grey prediction evolution algorithm for global optimization. Appl Math Model 79:145–160

    Article  MathSciNet  Google Scholar 

  3. Xu L, Wang J, Wang H et al (2020) BP neural network-based ABEP performance prediction for mobile Internet of Things communication systems. Neural Comput Appl 32(20):16025–16041

    Article  Google Scholar 

  4. Cai W, Li J, Xie Z, Zhao T et al (2018) Street object detection based on faster R-CNN. In: Chinese control conference (CCC), pp 9500–9503

  5. Dalal R, Moh T (2018) Fine-grained object detection using transfer learning and data augmentation. In: Advances in social networks analysis and mining (ASONAM), pp 893–896

  6. Dai J, Qi H, Xiong Y et al (2017) Deformable convolutional networks. In: International conference on computer vision, pp 764–773

  7. Xu L, Wang H, Gulliver TA (2020) Outage probability performance analysis and prediction for mobile IoV networks based on ICS-BP neural network. IEEE Internet Things J

  8. Mane S, Mangale S (2018) Moving object detection and tracking using convolutional neural networks. In: International conference on intelligent computing and control systems (ICICCS), pp 1809–1813

  9. Wu X, Hong D, Ghamisi P et al (2019) LW-ODF: a light-weight object detection framework for optical remote sensing imagery. In: International geoscience and remote sensing symposium (IGARSS), pp 1462–1465

  10. Kumaran N, Reddy US (2017) Object detection and tracking in crowd environment—a review. In: International conference on inventive computing and informatics (ICICI), pp 777–782

  11. Xu X, Hu ZB, Su Q et al (2020) Multivariable grey prediction evolution algorithm: a new metaheuristic. Appl Soft Comput 89:106086

    Article  Google Scholar 

  12. Zhang X, Zhu L (2019) Fast salient object detection based on multi-scale feature aggression. In: Chinese control and decision conference (CCDC), pp 5734–5738

  13. Zhao Z, Zheng P, Xu S et al (2019) Object detection with deep learning: a review. Trans Neural Netw Learn Syst 30:3212–3232

    Article  Google Scholar 

  14. Yang X, Fu K, Sun H et al (2018) Object detection with head direction in remote sensing images based on rotational region CNN. In: International geoscience and remote sensing symposium, pp 2507–2510

  15. Zhang Q, Wan C, S Bian (2018) Research on vehicle object detection method based on convolutional neural network. In: International symposium on computational intelligence and design (ISCID), pp 271–274

  16. Ross G, Jeff D, Trevor D et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Conference on computer vision and pattern recognition (CVPR), pp 580–587

  17. Ouadiay F Z, Bouftaih H, Bouyakhf EH et al (2018) Simultaneous object detection and localization using convolutional neural networks. In: Intelligent systems and computer vision (ISCV), pp 1–8

  18. He K, Zhang X, Ren S et al (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  19. Ross G (2015) Fast-CNN. In: IEEE international conference on computer vision (ICCV), pp 1440–1448

  20. Ren S, He K, Ross G et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural information processing systems, pp 91–99

  21. Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 6154–6162

  22. He K, Georgia G, Piot D et al (2020) Mask RCNN. IEEE Trans Pattern Anal Mach Intell 42(2):386–397. https://doi.org/10.1109/TPAMI.2018.2844175

  23. Joseph R, Santosh D, Ross G et al (2016) You only look once: unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788

  24. Redmom J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 6517–6525

  25. Xiao D, Shan F, Li Z et al (2019) An object detection model based on improved Tiny-Yolov3 under the environment of mining truck. IEEE Access 7:123757–123764

    Article  Google Scholar 

  26. Bochkovskiy A, Wang C Y, Liao H. YOLOv4: optimal speed and accuracy of object detection [EB/OL]. (2020-04-23) [2020-10-20]. http://ariv.xilesou.top/pdf/2004.10934

  27. Liu W, Anguelov D, Erhan D et al (2016) SSD: single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp 21–37

  28. Fu C Y, Liu W, Ranga A et al. DSSD: deconvolutional single shot detector [EB/OL] (2017-01-23) [2020-10-20]. http://ariv.xilesou.top/pdf/1701.06659.pdf

  29. Jeong J et al. Enhancement of SSD by concatenating feature maps for object detection [EB/OL] (2017-05-26) [2020-10-20]. http://ariv.xilesou.top/pdf/1705.09587.pdf

  30. Li Z, Zhou F. FSSD: feature fusion single shot multibox detector [EB/OL] (2018-05-17) [2020-10-20]. http://ariv.xilesou.top/pdf/1712.00960.pdf

  31. Lin TY, Goyal P, Girshick R et al (2017) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell PP(99):2999–3007

    Google Scholar 

  32. Law H, Deng J (2018) CornerNet: detecting objects as paired keypoints. In: European conference on computer vision, pp 642–656

  33. Zhou X, Wang D, Philipp K, et al. Objects as points [EB/OL]. (2019-04-25) [2020-10-20]. http://ariv.xilesou.top/pdf/1904.07850.pdf

  34. Sun K, Xiao B, Liu D et al (2019) Deep high-resolution representation learning for human pose estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1–12

  35. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778

  36. Tsung-Yi L, Piotr D, Ross G et al (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition, pp 936–944

Download references

Acknowledgements

This research was funded by National Natural Science Foundation of China (Grant No. 61702295), the Shandong Province Natural Science Foundation (Grant No. ZR2020QF003), the Opening Foundation of Key Laboratory of Opto-Technology and Intelligent Control (Lanzhou Jiaotong University), the Ministry of Education (Grant No. KFKT2020-09), the Shandong Province Postdoctoral Innovation Project (Grant No. 201703032), the Shandong Province Colleges and Universities Young Talents Initiation Program (Grant No. 2019KJN047), and the Doctoral Fund of QUST (Grant Nos. 1203043003480, 010029029).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Hui Li, Yan Dong, Lingwei Xu, Shujun Zhang & Junyin Wang

  2. Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan, 430074, China

    Lingwei Xu

  3. Key Laboratory of Opto-Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Lingwei Xu

Authors
  1. Hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingwei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shujun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junyin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lingwei Xu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Dong, Y., Xu, L. et al. Object detection method based on global feature augmentation and adaptive regression in IoT. Neural Comput & Applic 33, 4119–4131 (2021). https://doi.org/10.1007/s00521-020-05633-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05633-9

Keywords

Search

Navigation