An Edge Detection and Sliding Window Based Approach to Improve Object Localization in YOLOv3

  • Shaji Thorn Blue
  • M. BrindhaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1240)


Object detection is considered as a challenging field in computer vision. Once an object has been detected, the next challenge is object localization where a rectangular boundary box is drawn around the location of detected object. The proposed framework addresses the problem of object localization by improving its precision. You only look once or YOLOv3 is one of the well-known object detection algorithm with its state-of-the-art object detection and real time capabilities. Because of this reason, the proposed scheme uses YOLOv3 as the base algorithm. In this work, COCO dataset is used to detect an object, and to improve the precision of boundary box this work make use of edge detection, thresholding and morphological operation. Also, redundant edge removal algorithm is proposed to remove redundant edges and boundary box construction algorithm draws rectangular boundary box around detected object. When compared with YOLOv3, the proposed model produces significantly better results when boundary boxes around detected object is concern.


YOLOv3 Object detection Object localization Boundary box 


  1. 1.
    Druzhkov, P.N., Kustikova, V.D.: A survey of deep learning methods and software tools for image classification and object detection. Pattern Recogn. Image Anal. 26, 9–15 (2016). Scholar
  2. 2.
    Wojek, C., Dollar, P., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743–761 (2012)CrossRefGoogle Scholar
  3. 3.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  5. 5.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: International Conference on Image Processing (2002)Google Scholar
  6. 6.
    Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  7. 7.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)Google Scholar
  8. 8.
    Girshick, R.: Fast R-CNN. In: International Conference on Computer Vision (2015)Google Scholar
  9. 9.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)Google Scholar
  10. 10.
    Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: NIPS (2016)Google Scholar
  11. 11.
    Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)Google Scholar
  12. 12.
    He, K., et al.: Mask R-CNN. In: ICCV (2017)Google Scholar
  13. 13.
    Erhan, D., et al.: Scalable object detection using deep neural networks. In: CVPR (2014)Google Scholar
  14. 14.
    Najibi, M., et al.: G-CNN: an iterative grid based object detector. In: CVPR (2016)Google Scholar
  15. 15.
    Yoo, D., Park, S., et al.: AttentionNet: aggregating weak directions for accurate object detection. In: CVPR (2015)Google Scholar
  16. 16.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified real-time object detection. In: CVPR (2016)Google Scholar
  17. 17.
    Redmon, J., Farhadi, A.: YOLO9000: better faster stronger. In: CVPR (2016)Google Scholar
  18. 18.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). Scholar
  19. 19.
    Fu, C.-Y., et al.: DSSD: Deconvolutional single shot detector. (2017)
  20. 20.
    Shen, Z., Liu, Z., Li, J., Jiang, Y.-G., Chen, Y., Xue, X.: DSOD: learning deeply supervised object detectors from scratch. In: ICCV (2017)Google Scholar
  21. 21.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. (2018)
  22. 22.
    Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013). Scholar
  23. 23.
    Cortes, C., Vapnik, V.: Support vector network. Mach. Learn. 20, 273–297 (1995). Scholar
  24. 24.
    Blue, S.T., Brindha, M.: Edge detection based boundary box construction algorithm for improving the precision of object detection in YOLOv. In: 10th ICCCNT (2019)Google Scholar
  25. 25.
    Zhang, D., Zhang, P., Wang, L.: Cell counting algorithm based on YOLOv3 and image density estimation. In: 4th International Conference on Signal and Image Processing, (2019)Google Scholar
  26. 26.
    Zhang, X., Zhu, X.: Vehicle Detection in the aerial infrared images via an improved YOLOv3 network. In: 4th International Conference on Signal and Image Processing (2019)Google Scholar
  27. 27.
    Shi, T., Liu, M, Yang, Y., Wang, P., Huang, Y.: Fast classification and detection of marine targets in complex scenes with YOLOv3. In: OCEANS 2019, Marseille (2019)Google Scholar
  28. 28.
    Cui, H., Yang, Y., Liu, M., Shi, T., Qi, Q.: Ship detection: an improved YOLOv3 method. In: OCEANS 2019, Marseille (2019)Google Scholar
  29. 29.
    Qu, H., Yuan, T., Sheng, Z., Zhang, Y.: A pedestrian detection method based on YOLOv3 Model and Image enhanced by Retinex. In: 11th CISP-BMEI (2018)Google Scholar
  30. 30.
    Miao, F., Tian, Y., Jin, L.: Vehicle direction detection based on yolov3. In: 11th IHMSC (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology, TiruchirappalliTiruchirappalliIndia

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