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基于YOLOv3目標檢測的秧苗列中心線提取方法
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廣東省省級科技計劃項目(2014A020208018)


Extraction Method for Centerlines of Rice Seedings Based on YOLOv3 Target Detection
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    摘要:

    針對秧苗列中心線的檢測結(jié)果易受到水田中的浮萍,、藍藻以及水面反射,、風(fēng)速、光照情況等自然條件影響的問題,,提出一種基于YOLOv3目標檢測的秧苗列中心線檢測算法,。基于透視投影計算提取圖像的ROI(Region of interest)區(qū)域,,采用ROI圖像構(gòu)建數(shù)據(jù)集,,對YOLOv3模型進行訓(xùn)練,訓(xùn)練過程中通過減少YOLOv3模型的輸出降低運算量,,利用模型識別定位ROI內(nèi)的秧苗,,并輸出其檢測框,,對同列秧苗的檢測框進行自適應(yīng)聚類。在對秧苗圖像進行灰度化和濾波處理后,,在同類檢測框內(nèi)提取秧苗SUSAN(Smallest univalue segment assimilating nucleus)角點特征,,采用最小二乘法擬合秧苗列中心線。試驗結(jié)果表明,,該算法對于秧苗的不同生長時期,,以及在大風(fēng)、藍藻,、浮萍和秧苗倒影,、水面強光反射、暗光線的特殊場景下均能成功提取秧苗列中心線,,魯棒性較好,,模型的平均精度為91.47%,提取的水田秧苗列中心線平均角度誤差為0.97°,,單幅圖像(分辨率640像素×480像素)在GPU下的平均處理時間為82.6ms,,能夠滿足視覺導(dǎo)航的實時性要求。為復(fù)雜環(huán)境下作物中心線的提取提供了有效技術(shù)途徑,。

    Abstract:

    In order to extract the centerlines of rice seedlings, a new method based on YOLOv3 target detection algorithm was presented, which can extract centerlines of different growth stages of rice seedlings in complex paddy field so as to provide guide lines for autonomous navigation of robot. Firstly, an industrial camera which was 1 m high above the ground with pitch angles of 45° to 60° was used to capture image of rice seedlings, and then the region of interest (ROI) of the crop image was determined in order to find the instructive guide lines. Because of the perspective projection, the rice seedlings rows were labeled in segments. Then, the ROI images dataset was built to train YOLOv3 model. After that, the best YOLOv3 model was used to detect the rice seedling in the ROI and output bounding boxes. Secondly, the bounding boxes of the same rice seedlings row was clustered. Thirdly, image segmentation was applied and the smallest univalue segment assimilating nucleus (SUSAN) feature points was extracted within the bounding box of the same cluster. Finally, the least square method was applied in the algorithm to extract the centerlines of rice seedling. For complex paddy field environment such as windy weather, dark light, rice seedlings shadow and light reflection on water surface, as well as the impacts like duckweed and cyanobacteria, the proposed algorithm successfully and accurately extracted the centerlines of rice seedlings. For 200 test images, the mean average precision of trained network reached 91.47%, the mean average angle errors of the extracted centerlines was 0.97° and the average runtime of one image (resolution: 640 pixels×480 pixels) was 82.6ms. Compared with another method for centerlines extracting, this algorithm had higher robustness, higher accuracy and faster runtime. The result showed that the method was real time and had application values.

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張勤,王家輝,李彬.基于YOLOv3目標檢測的秧苗列中心線提取方法[J].農(nóng)業(yè)機械學(xué)報,2020,51(8):34-43. ZHANG Qin, WANG Jiahui, LI Bin. Extraction Method for Centerlines of Rice Seedings Based on YOLOv3 Target Detection[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(8):34-43.

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  • 收稿日期:2019-12-09
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  • 在線發(fā)布日期: 2020-08-10
  • 出版日期: 2020-08-10
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