An Intelligent Weed Recognition Method Based on Optical Patrol Image

doi:10.11930/j.issn.1004-9649.201902152

Abstract

Abstract: Power inspection is currently carried out mainly by drones. The weeds around the power equipment may cause potential safety hazards when the patrol images acquired by drones are used for patrol inspection, it is therefore necessary to recognize the weeds in the image. In this paper, a method for intelligent recognition of weeds is proposed for power patrol inspection based on optical patrol images. Based on the feature of weeds in the optical images, and combined with the convolutional neural network method, the problem of weed recognition near the power equipment in optical patrol images is solved. By amplifying and preprocessing the sample data of the optical patrol images, and introducing the region proposal network, the image features of the fixed number of candidate frames are extracted from the images. Then the network is connected to the improved image classification network to obtain a final convolutional neural network model. The experiments show that the accuracy rate can reach 97.98%, and the average time taken for detecting a 600×600 image is around 0.256 seconds, which meets the requirements of efficient recognition while ensuring accuracy.

Key words: patrol image, convolutional neural network, region proposal, image classification, weed recognition, artificial intelligence and big data application

CLC Number:

TM762
TP391

YUE Guoliang, LU Yanqiao, CHANG Hao, SUN Cuiying. An Intelligent Weed Recognition Method Based on Optical Patrol Image[J]. Electric Power, 2019, 52(11): 138-144,174.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201902152

https://www.electricpower.com.cn/EN/Y2019/V52/I11/138

References

[1] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2005:886-893.
[2] FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1):119-139.
[3] VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2001.
[4] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D. Cascade object detection with deformable part models[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2010:2241-2248.
[5] EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2):303-338.
[6] ZITNICK C L, DOLLÁR P. Edge boxes:locating object proposals from edges[C]//European Conference on Computer Vision-ECCV 2014, Cham:Springer, 2014:391-405.
[7] 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.
[8] REN S, HE K, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[9] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2016:779-788.
[10] LIU W, ANGUELOV D, ERHAN D, et al. SSD:single shot multibox detector[C]//European Conference on Computer Vision-ECCV 2016, Cham:Springer, 2016:21-37.
[11] ZHANG X, ZHOU X, LIN M, et al. Shufflenet:an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2018:6848-6856.
[12] CHOLLET F. Xception:deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2017:1800-1807.
[13] QI S, MA J, LIN J, et al. Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7):1451-1455.
[14] CORBANE C, NAJMAN L, PECOUL E, et al. A complete processing chain for ship detection using optical satellite imagery[J]. International Journal of Remote Sensing, 2010, 31(22):5837-5854.
[15] ZHU C, ZHOU H, WANG R, et al. A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(9):3446-3456.
[16] BI F, ZHU B, GAO L, et al. A visual search inspired computational model for ship detection in optical satellite images[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4):749-753.
[17] 孙俊, 何小飞, 谭文军, 等. 空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草[J]. 农业工程学报, 2018, 34(11):159-165 SUN Jun, HE Xiaofei, TAN Wenjun, et al. Recognition of crop seedling and weed recognition based on dilated convolution and global pooling in CNN[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(11):159-165
[18] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks:advances in neural information processing systems[C]//2012 TEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C.:IEEE Computer Society, 2012:1097-1105.
[19] 王璨, 武新惠, 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 Can, WU Xinhui, LI Zhiwei. 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
[20] 姜红花, 王鹏飞, 张昭, 等. 基于卷积网络和哈希码的玉米田间杂草快速识别方法[J]. 农业机械学报, 2018, 49(11):30-38 JIANG Honghua, WANG Pengfei, ZHANG Zhao, et al. Fast Identification of field weeds based on deep convolutional network and binary hash code[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(11):30-38
[21] 张旭, 王斌, 张旭. CNN在农田杂草图像识别的可行性探讨[J]. 电脑知识与技术, 2018, 14(22):187-189 ZHANG Xu, WANG Bin, ZHANG Xu. Discussion on feasibility of CNN with image recognition in farmland weeds[J]. Computer Knowledge and Technology, 2018, 14(22):187-189