Bullet Impact Detection in Silhouettes Using Mask R-CNN

Richar Fernandez Vilchez, David Mauricio

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The bullet impact detection in the silhouette plays an important role in the institutions which have a shooting range, specially where are regulated the license to carry a gun and where the evaluation manual process could present distortion in the ability's evaluation of the shooter and like a consequence the license's issuance. This paper proposes a method for an automatic detection of the bullet impacts in silhouettes based on deep learning and image processing, which consist of the following steps: pre-processing, impacts detection, edge detection and evaluation results. The experiments about 600 silhouettes with 2401 bullet impacts images of the proposed and implemented method considering the models Resnet 50 and Resnet 101 for Mask R-CNN show that Resnet 50 get better results than Resnet 101, achieving 97.6 %, 99.5 %, and 97.9 %, of accuracy, precision and recall, respectively, above the methods Circular Hough Transform, Circlet Detection, Random Sample Consensus, Randomized Hough Transform, Randomized Circle Detection, Support Vector Machine, Faster R-CNN, MnasNet and YOLO. Also, the results show 100 % of effectiveness in the edge detection and the count of the detected bullet impacts.

Original languageEnglish
Article number9139306
Pages (from-to)129542-129552
Number of pages11
JournalIEEE Access
StatePublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Edge detection
  • Mask R-CNN
  • bullet impact detection
  • image processing


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