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Semi Automated Bullet GroupAnalysis for Shooting Target Training
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Competitive Shooting as a sport is becoming famous these days and analysis of shooting group or bullet group which is a process of analysis location of bullet holes in one shooting session and stands as a metric for Precision of the weapon, Shooter's Accuracy, his Consistency and helps in finding Accurate load for the Cartridge. Knowledge of these factors can help in improving one's shooting and fine-tuning skills as a Shooter. Bullet group is alsoinuenced by the Accuracy of Rie, Optimal hand load, free run distance, environmental conditions like humidity, temperature, ambient light, windspeed, Shooter's position. Analyzing the Bullet group can be done in various ways, one way of doing it is by taking a Digital Image and analyzing the Image and detecting positions of bullet holes and Calculating metrics from this Metrics like Geometry of bullet group, largest distance between two bullets, compactness of the bullet group on target. In this work, detection of bullet holes is done by using these techniques: Template matching, Histogram equalization, White Balancing, Median andGaussian altering and Peak detection algorithms. After obtaining positions of the bullet holes in the Image. Complete Automation can be done by using the training the Algorithm with a Machine learning framework with the help of Articial neural networks. The existing bullet group analysis software require the bullet group shot on a specifc target, which limits the shooters to shoot on a target of shooter's choice every time and, those targets are not universal and vary from place to place. This algorithm aims to work on various types of target, and taking a step towards making a more generalized and more versatile algorithm.

Place, publisher, year, edition, pages
2018. , p. 52
Keywords [en]
Automated, Bullet group analysis, Template Matching, Peak Detection, Image Processing, Neural network
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-17046OAI: oai:DiVA.org:bth-17046DiVA, id: diva2:1251335
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASX Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Presentation
2018-04-19, 10:00 (English)
Supervisors
Examiners
Available from: 2018-10-01 Created: 2018-09-26 Last updated: 2018-10-01Bibliographically approved

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fulltext(8167 kB)58 downloads
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Type fulltextMimetype application/pdf

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Department of Applied Signal Processing
Signal Processing

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf