基于Adaboost算法的炮弹炸点检测

doi:10.3969/j.issn.1000-1093.2012.06.008

兵工学报 ›› 2012, Vol. 33 ›› Issue (6): 682-687.doi: 10.3969/j.issn.1000-1093.2012.06.008

基于Adaboost算法的炮弹炸点检测

秦晓燕，王晓芳，陈萍，储德军，王海涛

(陆军军官学院指挥自动化与仿真系，安徽合肥 230031)

收稿日期:2011-03-10 修回日期:2011-03-10 上线日期:2014-03-04
作者简介:秦晓燕（1980—），女，讲师
基金资助:
炮兵学院科研学术基金项目（2010XYJJ-060）

Artillery Blast Point Detection Based on Adaboost Algorithm

QIN Xiao-yan, WANG Xiao-fang, CHEN Ping, CHU De-jun, WANG Hai-tao

(Department of Command Automation and Simulation, Army Officer Academy, Hefei 230031, Anhui, China)

Received:2011-03-10 Revised:2011-03-10 Online:2014-03-04

摘要/Abstract

摘要： 炮弹炸点检测是射击校射、毁伤效果评估和敌方火力位置估计的前提和基础。根据炮弹炸点的特点，对Haar特征进行了扩展，增加了一种中心环绕的Haar特征，并给出了其快速计算方法。以Adaboost算法为框架构建了一种炸点检测算法，采用真实炸点图像对提出的算法进行了实验验证，结果表明：与未扩展特征的Adaboost算法相比，本文算法对炸点类目标具有较高的检测性能。

关键词: 信息处理技术, 炸点检测, 积分图, Adaboost算法, Haar特征

Abstract: The blast point detection is a foundation of fire emendation, damage evaluation and opposite firepower point estimation. According to the characteristics of artillery blast point, this paper proposes a new center-surround Haar-like feature which can be calculated quickly. An algorithm for the blast point detection is presented based on Adaboost with the new Haar-like feature. The experiment results show that the proposed algorithm has better performance than the Adaboost algorithm without the new Haar-like feature.

中图分类号:

TP301.6

秦晓燕，王晓芳，陈萍，储德军，王海涛. 基于Adaboost算法的炮弹炸点检测[J]. 兵工学报, 2012, 33(6): 682-687.

QIN Xiao-yan, WANG Xiao-fang, CHEN Ping, CHU De-jun, WANG Hai-tao. Artillery Blast Point Detection Based on Adaboost Algorithm[J]. Acta Armamentarii, 2012, 33(6): 682-687.

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基于Adaboost算法的炮弹炸点检测

Artillery Blast Point Detection Based on Adaboost Algorithm

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