[1]WEI Wenge,TAN Xiaoyang.Highly similar wood blocks detection under dense stacking[J].CAAI Transactions on Intelligent Systems,2019,14(4):642-649.[doi:10.11992/tis.201806001]
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Highly similar wood blocks detection under dense stacking

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 642-649 Column: 学术论文—机器学习 Public date: 2019-07-02
Title:
Highly similar wood blocks detection under dense stacking
Author(s):
WEI Wen’ge1; 2;  TAN Xiaoyang1; 2
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China
Keywords:
dense stacking; highly similar; cross section of wood; detection; production of wood block; loss function; robustness
CLC:
TP181
DOI:
10.11992/tis.201806001
Abstract:
Quick and effective detection of wood blocks cross section and acquisition of the required information are the key to improving the efficiency of wood block production and transactions. However, since wood blocks are often densely stacked, their cross sections have high similarity, while their boundaries are not obvious; this poses a great challenge in detecting the cross sections and acquiring the required information. Considering the difficulty in detecting the cross section of highly similar wood blocks under dense stacking, a simple and efficient wood R-CNN network model is proposed in this paper. The detection accuracy is increased by improving the model loss function and the non-maximum suppression algorithm, and detection speed is ensured by simplifying the network structure and modifying the feature pyramid network. A series of experiments prove that the algorithm model can precisely detect the cross sections of blocks with high similarity under dense stacking. Moreover, it can guarantee fast detection speed and good robustness and can be actually used in the production and transaction of wood blocks.
References:
[1] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014:580-587.
[2] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile, 2015:1440-1448.
[3] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada, 2015:91-99.
[4] HE Kaiming, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017:2980-2988.
[5] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//European Conference on Computer Vision. Zurich, Switzerland, 2014:346-361.
[6] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD:Single shot multibox detector[C]//European Conference on Computer Vision. Amsterdam, The Netherlands, 2016:21-37.
[7] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:779-788.
[8] REDMON J, FARHADI A. YOLO9000:better, faster, stronger[J]. arXiv:1612.08242, 2016.
[9] UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2):154-171.
[10] 于文勇, 王立海, 杨慧敏, 等. 超声波木材缺陷检测若干问题的探讨[J]. 森林工程, 2006, 22(6):7-9 YU Wenyong, WANG Lihai, YANG Huimin, et al. The study of wood defects detection based on ultrasonic techniques[J]. Forest engineering, 2006, 22(6):7-9
[11] 杨春梅, 胡万义, 白帆, 等. 木材缺陷检测理论及方法的发展[J]. 林业机械与木工设备, 2004, 32(3):8-10 YANG Chunmei, HU Wanyi, BAI Fan, et al. The development of theory and method in assaying wood defection[J]. Forestry machinery and woodworking equipment, 2004, 32(3):8-10
[12] 戴天虹, 李琳, 解朦. 基于四元数矩阵奇异值分解的木材缺陷检测分析[J]. 森林工程, 2014, 30(1):52-55, 59 DAI Tianhong, LI Lin, XIE Meng. Wood defect detection and analysis based on quaternion matrix singular value decomposition[J]. Forest engineering, 2014, 30(1):52-55, 59
[13] 梁善庆, 蔡智勇, 王喜平, 等. 北美木材无损检测技术的研究与应用[J]. 木材工业, 2008, 22(3):5-8 LIANG Shanqing, CAI Zhiyong, WANG Xiping, et al. Developments and applications of nondestructive tests for wood in North America[J]. China wood industry, 2008, 22(3):5-8
[14] 戚大伟, 牟洪波. 基于Hu不变矩和BP神经网络的木材缺陷检测[J]. 东南大学学报(自然科学版), 2013, 43(S1):63-66 QI Dawei, Mu Hongbo. Detection of wood defects types based on Hu invariant moments and BP neural network[J]. Journal of Southeast University (natural science edition), 2013, 43(S1):63-66
[15] 戚大伟, 牟洪波. 人工神经网络在木材缺陷检测中的应用[J]. 森林工程, 2006, 22(1):21-23 QI Dawei, MU Hongbo. Application of artificial neural networks in the testing of wood defects[J]. Forest engineering, 2006, 22(1):21-23
[16] 牟洪波. 基于人工神经网络的木材缺陷检测研究[D]. 哈尔滨:东北林业大学, 2006. MU Hongbo. Study on wood defects testing based on artificial neural network[D]. Harbin:Northeast Forestry University, 2006.
[17] 夏春江, 王培良, 张媛. 基于深度学习的木材含水率预测[J]. 杭州电子科技大学学报(自然科学版), 2015, 35(1):31-35 XIA Chunjiang, WANG Peiliang, ZHANG Yuan. Prediction of moisture content of wood based on deep learning[J]. Journal of Hangzhou Dianzi University (natural sciences), 2015, 35(1):31-35
[18] 崔金刚. 图像处理技术在木材检测方面的应用[D]. 哈尔滨:东北林业大学, 2007. CUI Jingang. The digital image processing using on wood detests testing[D]. Harbin:Northeast Forestry University, 2007.
[19] 戚大伟, 乔景禄, 张杰. 图象处理技术在木材年轮检测中的应用[J]. 东北林业大学学报, 1994, 22(2):106-111 QI Dawei, QIAO Jinglu, ZHANG Jie. Image processing technique on wooden annual rings x-ray picture[J]. Journal of Northeast Forestry University, 1994, 22(2):106-111
[20] 徐华东, 王立海, 倪松远. 模态分析技术在木材性质检测中应用的研究进展[J]. 森林工程, 2007, 23(6):15-17 XU Huadong, WANG Lihai, NI Songyuan. Literature review on application of modal analysis technology in the wood testing[J]. Forest engineering, 2007, 23(6):15-17
[21] 杨学春, 王立海. 应力波技术在木材性质检测中的研究进展[J]. 森林工程, 2002, 18(6):11-12 YANG Xuechun, WANG Lihai. Research progress of testing timber characteristics by using stress wave technique[J]. Forest engineering, 2002, 18(6):11-12
[22] 冯海林, 李光辉, 方益明, 等. 应力波传播模型及其在木材检测中的应用[J]. 系统仿真学报, 2010, 22(6):1490-1493 FENG Hailin, LI Guanghui, FANG Yiming, et al. Stress wave propagation modeling and application in wood testing[J]. Journal of system simulation, 2010, 22(6):1490-1493
[23] 韩书霞, 于雷, 孔超, 等. 基于计算机断层扫描技术的木材密度检测研究[J]. 森林工程, 2007, 23(1):19-23 HAN Shuxia, YU Lei, KONG Chao, et al. Research on wood density testing based on computer tomography technology[J]. Forest engineering, 2007, 23(1):19-23
[24] 王立海, 孙天用. 木材腐朽检测及防治的研究进展[J]. 黑龙江大学工程学报, 2011, 2(2):63-69 WANG Lihai, SUN Tianyong. Research on progressing of testing and prevention of wood decay[J]. Journal of Engineering of Heilongjiang University, 2011, 2(2):63-69
[25] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:770-778.
[26] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:936-944.
[27] NEUBECK A, VAN GOOL L. Efficient non-maximum suppression[C]//18th International Conference on Pattern Recognition. Hong Kong, China, 2006:850-855.
[28] XIE Saining, GIRSHICK R, DOLLáR P, et al. Aggregated residual transformations for deep neural networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:5987-5995.
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