哈尔滨工程大学学报
哈爾濱工程大學學報
합이빈공정대학학보
JOURNAL OF HARBIN ENGINEERING UNIVERSITY
2014年
2期
148-154
,共7页
万磊%黄蜀玲%张铁栋%王博
萬磊%黃蜀玲%張鐵棟%王博
만뢰%황촉령%장철동%왕박
目标识别%前视声呐%距离选通激光成像系统%小波矩%特征选择
目標識彆%前視聲吶%距離選通激光成像繫統%小波矩%特徵選擇
목표식별%전시성눌%거리선통격광성상계통%소파구%특정선택
object recognition%forward looking sonar%underwater laser gated system%wavelet moment%feature se-lection
由于水体对可见光的衰减和散射较强,为克服传统CCD摄像机所得图像的低对比度、以及低信噪比的缺陷,提出以距离选通激光成像设备和前视声呐为传感器建立水下目标识别系统。通过前视声呐图像获取目标的距离信息,自主调节激光成像设备的接收摄像机与目标的距离,克服了水下机器人的距离选通激光图像自动采集的困难。对传统小波矩进行改进,获得反映目标全局和局部信息的具有旋转、平移、缩放不变性的小波矩,通过类内特征的均值和方差建立了特征选择模型。以特征选择后的小波矩作为广义回归神经网络GRNN的输入向量,对6类水池实测目标进行识别。试验结果表明建立的自主式水下机器人的目标识别系统具有较好的识别率,验证了所建系统的有效性和可行性。
由于水體對可見光的衰減和散射較彊,為剋服傳統CCD攝像機所得圖像的低對比度、以及低信譟比的缺陷,提齣以距離選通激光成像設備和前視聲吶為傳感器建立水下目標識彆繫統。通過前視聲吶圖像穫取目標的距離信息,自主調節激光成像設備的接收攝像機與目標的距離,剋服瞭水下機器人的距離選通激光圖像自動採集的睏難。對傳統小波矩進行改進,穫得反映目標全跼和跼部信息的具有鏇轉、平移、縮放不變性的小波矩,通過類內特徵的均值和方差建立瞭特徵選擇模型。以特徵選擇後的小波矩作為廣義迴歸神經網絡GRNN的輸入嚮量,對6類水池實測目標進行識彆。試驗結果錶明建立的自主式水下機器人的目標識彆繫統具有較好的識彆率,驗證瞭所建繫統的有效性和可行性。
유우수체대가견광적쇠감화산사교강,위극복전통CCD섭상궤소득도상적저대비도、이급저신조비적결함,제출이거리선통격광성상설비화전시성눌위전감기건립수하목표식별계통。통과전시성눌도상획취목표적거리신식,자주조절격광성상설비적접수섭상궤여목표적거리,극복료수하궤기인적거리선통격광도상자동채집적곤난。대전통소파구진행개진,획득반영목표전국화국부신식적구유선전、평이、축방불변성적소파구,통과류내특정적균치화방차건립료특정선택모형。이특정선택후적소파구작위엄의회귀신경망락GRNN적수입향량,대6류수지실측목표진행식별。시험결과표명건립적자주식수하궤기인적목표식별계통구유교호적식별솔,험증료소건계통적유효성화가행성。
Water has serious effects on the attenuation and scattering of visible light. In order to overcome the de-fects of the images captured by a conventional CCD camera with low contrast and a low signal-to-noise ratio, it is proposed that an underwater object recognition system be established with the underwater laser gated system and the forward looking sonar as the sensor. Through the image obtained by the forward looking sonar, the object distance information may be gained, the distance between the receiving camera of the laser imaging system and the object may be autonomously regulated, so as to overcome the difficulty of automatic acquisition for the range-gated laser image of the underwater vehicle. The conventional wavelet moment is improved to acquire a wavelet moment with the properties including rotation, horizontal movement and invariant scaling, which reflects the global and local in-formation of the object. A feature selection model is proposed for the mean and variance of the inside-category fea-ture, the wavelet moments after feature selection are used as the input vector of the generalized regression neural network GRNN for the recognition of six types of pool actually-measured objects. The test results show that the es-tablished object recognition system of the autonomous underwater vehicle has an excellent recognition rate and as a result the established system is effective and feasible.