采摘機(jī)器人目標(biāo)識別及定位研究
發(fā)布時(shí)間：2018-11-23 19:14
【摘要】：在自然環(huán)境下橘子目標(biāo)所處的背景十分復(fù)雜,被枝葉遮擋或者果實(shí)之間疊加的現(xiàn)象非常普遍,這種環(huán)境的復(fù)雜性無疑給機(jī)器視覺系統(tǒng)的識別帶來困難,導(dǎo)致采摘機(jī)器人不能有效、準(zhǔn)確地識別目標(biāo)。針對這一問題,本文對復(fù)雜環(huán)境中橘子目標(biāo)圖像的識別與定位問題進(jìn)行了仿真與實(shí)驗(yàn)研究。主要工作內(nèi)容如下:對復(fù)雜環(huán)境中橘子目標(biāo)輪廓的識別方法進(jìn)行了研究。介紹了傳統(tǒng)的邊緣檢測算法,并對橘子圖像進(jìn)行了測試,測試效果顯示該方法不能有效提取復(fù)雜環(huán)境中的橘子目標(biāo)輪廓。將K-means聚類算法與Canny算法融合,采用K-means聚類算法從目標(biāo)圖像中分割目標(biāo)物區(qū)域,結(jié)合Canny檢測算法檢測出目標(biāo)物區(qū)域的輪廓,進(jìn)而完成目標(biāo)的識別,橘子圖像測試結(jié)果驗(yàn)證了該方法的有效性。對重疊橘子目標(biāo)輪廓分離方法進(jìn)行了研究。在對腐蝕剝離法及分水嶺分割法等方法分離重疊(鄰接)目標(biāo)的原理和特點(diǎn)比較基礎(chǔ)上,研究了基于K-means聚類算法分離重疊橘子目標(biāo)輪廓的方法,該方法對雙果鄰接、重疊的橘子目標(biāo)圖像進(jìn)行了測試,測試結(jié)果看出重疊目標(biāo)輪廓分離完整,體現(xiàn)了該方法的有效性。對橘子目標(biāo)輪廓匹配進(jìn)行了研究。為描述目標(biāo)輪廓特征,引入幾何不變矩參數(shù)作為輪廓的描述子,采用作差法的結(jié)果作為兩幅圖像中輪廓匹配測度值,實(shí)驗(yàn)數(shù)據(jù)表明,幾何不變矩參數(shù)在橘子目標(biāo)輪廓特征描述方面具有較好的效果,匹配能力良好。同時(shí)引入基于梯度法的Hough變換圓檢測方法對類圓形橘子目標(biāo)輪廓擬合重建,測試效果圖顯示該方法能夠?qū)崿F(xiàn)果實(shí)的有效定位。對基于單目視覺的目標(biāo)深度進(jìn)行了計(jì)算。移動(dòng)攝像機(jī)采集同一場景下的兩幅圖像,提取相匹配的特征點(diǎn),結(jié)合攝像機(jī)成像原理,計(jì)算出空間目標(biāo)物距離攝像機(jī)的深度信息。最后,介紹了實(shí)驗(yàn)硬件系統(tǒng),分別以橘子和大棗為實(shí)驗(yàn)對象,完成了復(fù)雜環(huán)境中目標(biāo)輪廓的識別與定位實(shí)驗(yàn),驗(yàn)證了本文方法的有效性。
[Abstract]:In the natural environment, the background of orange target is very complex, and it is very common to be occluded by branches or leaves or superimposed between fruits. The complexity of this environment undoubtedly makes it difficult to recognize the machine vision system. As a result, the picking robot can not recognize the target effectively and accurately. In order to solve this problem, the recognition and localization of orange target image in complex environment are studied by simulation and experiment. The main work is as follows: the recognition method of orange target contour in complex environment is studied. This paper introduces the traditional edge detection algorithm and tests the orange image. The test results show that the method can not effectively extract the orange target contour in complex environment. The K-means clustering algorithm and the Canny algorithm are fused, and the K-means clustering algorithm is used to segment the object region from the target image. The contour of the target region is detected by combining the Canny detection algorithm, and the target recognition is accomplished. The results of orange image test show that the proposed method is effective. The separation method of overlapping orange target contour was studied. On the basis of comparing the principle and characteristics of separating overlapping (adjacent) targets by corrosive stripping method and watershed segmentation method, the method of separating overlapping orange target contour based on K-means clustering algorithm is studied. The overlapping orange target images are tested, and the results show that the overlapping targets are separated completely, which shows the effectiveness of the method. The object contour matching of orange was studied. In order to describe the contour feature of the target, the geometric moment invariant parameter is introduced as the descriptor of the contour, and the result of the difference method is used as the contour matching measure value in the two images. The experimental data show that, The geometric moment invariant parameters have good performance in describing the contour feature of orange target, and the matching ability is good. At the same time, the Hough transform circle detection method based on gradient method is introduced to reconstruct the contour of the circular orange target. The test results show that the method can effectively locate the fruit. The target depth based on monocular vision is calculated. Moving camera collects two images in the same scene, extracts matching feature points, and calculates the depth information of the space object distance from the camera in combination with the principle of camera imaging. Finally, the hardware system of the experiment is introduced. Taking orange and jujube as experimental objects, the recognition and localization experiments of target contour in complex environment are carried out, and the validity of this method is verified.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242
本文編號：2352458
[Abstract]:In the natural environment, the background of orange target is very complex, and it is very common to be occluded by branches or leaves or superimposed between fruits. The complexity of this environment undoubtedly makes it difficult to recognize the machine vision system. As a result, the picking robot can not recognize the target effectively and accurately. In order to solve this problem, the recognition and localization of orange target image in complex environment are studied by simulation and experiment. The main work is as follows: the recognition method of orange target contour in complex environment is studied. This paper introduces the traditional edge detection algorithm and tests the orange image. The test results show that the method can not effectively extract the orange target contour in complex environment. The K-means clustering algorithm and the Canny algorithm are fused, and the K-means clustering algorithm is used to segment the object region from the target image. The contour of the target region is detected by combining the Canny detection algorithm, and the target recognition is accomplished. The results of orange image test show that the proposed method is effective. The separation method of overlapping orange target contour was studied. On the basis of comparing the principle and characteristics of separating overlapping (adjacent) targets by corrosive stripping method and watershed segmentation method, the method of separating overlapping orange target contour based on K-means clustering algorithm is studied. The overlapping orange target images are tested, and the results show that the overlapping targets are separated completely, which shows the effectiveness of the method. The object contour matching of orange was studied. In order to describe the contour feature of the target, the geometric moment invariant parameter is introduced as the descriptor of the contour, and the result of the difference method is used as the contour matching measure value in the two images. The experimental data show that, The geometric moment invariant parameters have good performance in describing the contour feature of orange target, and the matching ability is good. At the same time, the Hough transform circle detection method based on gradient method is introduced to reconstruct the contour of the circular orange target. The test results show that the method can effectively locate the fruit. The target depth based on monocular vision is calculated. Moving camera collects two images in the same scene, extracts matching feature points, and calculates the depth information of the space object distance from the camera in combination with the principle of camera imaging. Finally, the hardware system of the experiment is introduced. Taking orange and jujube as experimental objects, the recognition and localization experiments of target contour in complex environment are carried out, and the validity of this method is verified.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242
【引證文獻(xiàn)】
相關(guān)期刊論文 前1條
1 初廣麗;張偉;王延杰;丁南南;劉艷瀅;;基于機(jī)器視覺的水果采摘機(jī)器人目標(biāo)識別方法[J];中國農(nóng)機(jī)化學(xué)報(bào);2018年02期
，本文編號：2352458
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