[1] Sabzi S, Abbaspour-Gilandeh Y. Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and particle swarm algorithm[J]. Measurement, 2018, 126: 22-36.

[2] 白敬, 徐友, 魏新华, 等. 基于光谱特性分析的冬油菜苗期田间杂草识别[J]. 农业工程学报, 2013, 29(20): 128-134.

    Bai J, Xu Y, Wei X H, et al. Weed identification from winter rape at seedling stage based on spectrum characteristics analysis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(20): 128-134.

[3] 潘冉冉, 骆一凡, 王昌, 等. 高光谱成像的油菜和杂草分类方法[J]. 光谱学与光谱分析, 2017, 37(11): 3567-3572.

    Pan R R, Luo Y F, Wang C, et al. Classifications of oilseed rape and weeds based on hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2017, 37(11): 3567-3572.

[4] 翟瑞芳, 方益杭, 林承达, 等. 基于高斯HI颜色算法的大田油菜图像分割[J]. 农业工程学报, 2016, 32(8): 142-147.

    Zhai R F, Fang Y H, Lin C D, et al. Segmentation of field rapeseed plant image based on Gaussian HI color algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(8): 142-147.

[5] Bakhshipour A, Jafari A. Evaluation of support vector machine and artificial neural networks in weed detection using shape features[J]. Computers and Electronics in Agriculture, 2018, 145: 153-160.

[6] 胡炼, 罗锡文, 曾山, 等. 基于机器视觉的株间机械除草装置的作物识别与定位方法[J]. 农业工程学报, 2013, 29(10): 12-18.

    Hu L, Luo X W, Zeng S, et al. Plant recognition and localization for intra-row mechanical weeding device based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(10): 12-18.

[7] 吴兰兰, 徐恺, 熊利荣. 基于视觉注意模型的苗期油菜田间杂草检测[J]. 华中农业大学学报, 2018, 37(2): 96-102.

    Wu L L, Xu K, Xiong L R. Detecting weed in seedling rapeseed oil field based on visual-attention model[J]. Journal of Huazhong Agricultural University, 2018, 37(2): 96-102.

[8] Chang C L, Lin K M. Smart agricultural machine with a computer vision-based weeding and variable-rate irrigation scheme[J]. Robotics, 2018, 7(3): 38.

[9] 王林, 张鹤鹤. Faster R-CNN模型在车辆检测中的应用[J]. 计算机应用, 2018, 38(3): 666-670.

    Wang L, Zhang H H. Application of Faster R-CNN model in vehicle detection[J]. Journal of Computer Applications, 2018, 38(3): 666-670.

[10] 王璨, 武新慧, 李志伟. 基于卷积神经网络提取多尺度分层特征识别玉米杂草[J]. 农业工程学报, 2018, 34(5): 144-151.

    Wang C, Wu X H, Li Z W. Recognition of maize and weed based on multi-scale hierarchical features extracted by convolutional neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(5): 144-151.

[11] 周云成, 许童羽, 郑伟, 等. 基于深度卷积神经网络的番茄主要器官分类识别方法[J]. 农业工程学报, 2017, 33(15): 219-226.

    Zhou Y C, Xu T Y, Zheng W, et al. Classification and recognition approaches of tomato main organs based on DCNN[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(15): 219-226.

[12] 王丹丹, 何东健. 基于R-FCN深度卷积神经网络的机器人疏果前苹果目标的识别[J]. 农业工程学报, 2019, 35(3): 156-163.

    Wang D D, He D J. Recognition of apple targets before fruits thinning by robot based on R-FCN deep convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(3): 156-163.

[13] GirshickR, DonahueJ, DarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio, USA. New York: IEEE, 2014: 580- 587.

[14] GirshickR. Fast R-CNN[C]∥Proceedings of the 2015 IEEE Conference on Computer Vision, December 11-18, 2015, Santiago, Chile. New York: IEEE, 2015: 1440- 1448.

[15] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(9): 1904-1916.

[16] Ren SQ, He KM, GirshickR, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]∥Advances in Neural Information Processing Systems (NIPS 2015), December 7-12, 2015, Montreal, Quebec, Canada. Canada: NIPS, 2015: 91- 99.

[17] 王金传, 谭喜成, 王召海, 等. 基于Faster R-CNN深度网络的遥感影像目标识别方法研究[J]. 地球信息科学学报, 2018, 20(10): 1500-1508.

    Wang J C, Tan X C, Wang Z H, et al. Faster R-CNN deep learning network based object recognition of remote sensing image[J]. Journal of Geo-Information Science, 2018, 20(10): 1500-1508.

[18] 曹宇剑, 徐国明, 史国川. 基于旋转不变Faster R-CNN的低空装甲目标检测[J]. 激光与光电子学进展, 2018, 55(10): 101501.

    Cao Y J, Xu G M, Shi G C. Low altitude armored target detection based on rotation invariant Faster R-CNN[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101501.

[19] 马晓云, 朱丹, 金晨, 等. 基于改进Faster R-CNN的子弹外观缺陷检测[J]. 激光与光电子学进展, 2019, 56(15): 151202.

    Ma X Y, Zhu D, Jin C, et al. Bullet appearance defect detection based on improved faster region-convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 151202.

[20] 陈恩加, 唐向宏, 傅博文. Faster R-CNN行人检测与再识别为一体的行人检索算法[J]. 计算机辅助设计与图形学学报, 2019, 31(2): 332-339.

    Chen E J, Tang X H, Fu B W. Pedestrian search method based on Faster R-CNN with the integration of pedestrian detection and re-identification[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(2): 332-339.

[21] 田青, 袁曈阳, 杨丹, 等. 基于暗通道去雾和深度学习的行人检测方法[J]. 激光与光电子学进展, 2018, 55(11): 111007.

    Tian Q, Yuan T Y, Yang D, et al. A pedestrian detection method based on dark channel defogging and deep learning[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111007.

[22] 孙哲, 张春龙, 葛鲁镇, 等. 基于Faster R-CNN的田间西兰花幼苗图像检测方法[J]. 农业机械学报, 2019, 50(7): 216-221.

    Sun Z, Zhang C L, Ge L Z, et al. Image detection method for broccoli seedlings in field based on Faster R-CNN[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(7): 216-221.

[23] LiuW, AnguelovD, ErhanD, et al. SSD: single shot MultiBox detector[M] ∥Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9905: 21- 37.

[24] 冯小雨, 梅卫, 胡大帅. 基于改进Faster R-CNN的空中目标检测[J]. 光学学报, 2018, 38(6): 0615004.

    Feng X Y, Mei W, Hu D S. Aerial target detection based on improved Faster R-CNN[J]. Acta Optica Sinica, 2018, 38(6): 0615004.

[25] Lin TY, DollarP, GirshickR, et al. Feature pyramid networks for object detection[C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, Hawaii, USA. New York: IEEE, 2017: 2117- 2125.