基于二维激光与图像的人工林采育目标检测方法

doi:10.11707/j.1001-7488.20150714

Abstract

Abstract:

[Objective] When large forestry equipment are in practice, it needs operator to make a lot of observation to determine due to the complexity of forest environments and the impact of obstacles, which result in intermittent operation, reducing operational efficiency. Especially in poor light conditions, it will greatly increase the risks for the operator. Based on 2D laser and images, harvesting targets in artificial forest were detected and identified in this study by using information fusion technology, which provided supplementary information to the operator to compensate for the lack of the human eyes. [Method] This paper presented a target detection method for artificial forest harvesting based on 2D laser and images. The main contents were: 1) Built harvesting target information collection system based on 2D laser scanner and thermal imager, where PC was applied to control the information collection. Also the collected signal pretreatment was conducted in this part. Then laser data, visible and infrared images were captured for targets. 2) Internal and external joint calibration was conducted between laser points and image to match them together, thereby obtaining the target area image we need at the same time to obtain the coordinate position of the target by the laser information, which provided the basis for subsequent target recognition. 3) Fused the visible image and infrared image. Fused image had richer information and reduced uncertainty caused by a single sensor. 4) According to the collected information, features including temperature, colors and shape were extracted to provide specific evidence for target identification. 5) On the basis of obtained features, a popular machine learning algorithms-support vector machineswas applied by training and learning with a number of samples to establish math model for features and targets. 6) The characteristic parameters of the mathematical model were optimized by 3 different optimization algorithms to improve recognition performance. [Result] Models with different optimizations were compared by ROC curves. Experiment results showed that the optimized model by GA used in this paper could recognize harvesting targets effectively and it’s correct rate could reach more than 96%. [Conclusion] Multi-sensor fusion technology was introduced into smart forestry equipment in this paper. Instead of the commonly used expensive 3D laser scanning system, 2D laser and images were combined and used. On the one hand, it saved the costs; on the other hand, for the 2D data, the data processing was faster. At the same time, the complementary among sensorsmade the measuring and identification more accurate. The results showed that the method used in this study could provide the information needed by forestry equipment operations, which improved operational efficiency and reduced operational risks.

Key words: laser, image, harvesting target, recognition, detection

CLC Number:

S792.39

Ding Xiaokang, Yan Lei, Kong Jianlei, Liu Jinhao. A Target Detection Method for Artifical Harvesting Based on 2D Laser and Images[J]. Scientia Silvae Sinicae, 2015, 51(7): 129-135.

References

韩光瞬, 冯仲科, 刘永霞, 等. 2005.三维激光扫描系统测树原理及精度分析. 北京林业大学学报, 27(S2): 187-190.(Han G S, Feng Z K, Liu Y X, et al. 2005. Forest measurement principles and precision analysis of three-dimensional laser scanning system. Journal of Beijing Forestry University, 27(S2): 187-190.[in Chinese])
刘晋浩, 王丹. 2006. 谈国内外人工林抚育机械的现状及发展趋势. 森林工程,22(3):12-14.(Liu J H, Wang D. 2006.The actuality and development trend of plantation tending machines at home and abroad. Forest Engineering, 22(3):12-14.[in Chinese])
罗旭, 冯仲科,邓向瑞,等. 2007. 三维激光扫描成像系统在森林计测中的应用. 北京林业大学学报, 29(S2): 82-87.(Luo X, Feng Z K, Deng X R, et al. 2007. Application of 3D laser scanning imaging system in forest measuring. Journal of Beijing Forestry University, 29(S2): 82-87.[in Chinese])
王典, 刘晋浩, 王建利. 2011. 基于系统聚类的林地内采育目标识别与分类. 农业工程学报, 27(12): 173-177.(Wang D, Liu J H, Wang J L. 2011. Identification and classification of scanned target in forest based on hierarchical cluster. Transactions of the Chinese Society of Agricultural Engineering, 27(12): 173-177.[in Chinese])
吴春峰, 陆怀民. 2009. 基于激光扫描的立木材积测定关键技术研究. 哈尔滨: 东北林业大学硕士学位论文.(Wu C F, Lu H M. 2009.Research of measuring standing volume based on laser scanning system. Harbin: MS thesis of Northeast Forestry University.[in Chinese])
Aschoff T,Spiecker H. 2004.Algorithms for the automatic detection of trees in laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, 36(8):71-75.
Aschoff T, Thies M, Spiecker H. 2004.Describing forest stands using terrestriallaser-scanning. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(5): 237-241.
Bouguet J Y.2013. Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/parameters.html.
Chen P W, Wang J Y, Lee H. Model selection of SVMs using GA approach. Proc of 2004 IEEE Int Joint Conf on Neural Networks, 2035-2040.
Do M N, Vetterli M.2002.Contourlets: a directional multi-resolution image representation. Proceedings 2002 International Conference on Image processing, 357-360.
Eberhart R, Kenney J. 1995.A new optimizer using particle swarm theory. Proc of the Sixth International Symposium on Micro Machine and Human Science, 39-43.
Hopkinson C, Chasmer L,Young-Pow C,et al. 2004.Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research,34(3):573-583.
Jutila J, Kannas K, Visala A.2007. Tree measurement in forest by 2D laser scanning. International Symposium on Computational Intelligence in Robotics and Automation, 491-496.
Király G, Brolly G. 2007.Tree height estimation methods for terrestrial laser scanning in a forest reserve. ISPRS Workshop on Laser Scanning 2007 and Silvi Laser 2007,36(3):211-215.
Király G, Brolly G. 2008.Modelling single trees from terrestrial laser scanning data in a forest reserve. The Photogrammetric Journal of Finland, 21(1):37-50.
Liu X L, Jia D X, Li H. 2010.Research on Kernel parameter optimization of support vector machine in speaker recognition. Science Technology and Engineering, 10(7): 1669-1673.
Miettinen M, Ohman M, Visala A, et al. 2007. Simultaneous localization and mapping for forest harvesters. IEEE International Conference on Robotics and Automation, 517-522.
Ohman M, Miettinen M, Kannas K, et al.2007. Tree measurement and simultaneous localization and mapping system for forest harvesters. The 6th International Conference on Field and Service Robotics, 369-378.
Pfeifer N, Gorte B, Winterhalder D.2004. Automatic reconstruction of single trees from terrestrial laser scanner data. ISPRS XXth Congress, 114-119.
Simonse M, Aschoff T, Spiecker H, et al.2003.Automatic determination of forest inventory parameters using terrestrial laser scanning. Institute for Forest Growth, Freiburg, Germany, 1-7.
Thies M, Pfeifer N, Winterhalder D, et al.2004.Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Journal of Forest Research, 19(6): 571-581.
Vapnik V.1995.The nature of statistical learning theory.New York: Springer.

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A Target Detection Method for Artifical Harvesting Based on 2D Laser and Images

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