融合FPN的Faster R-CNN复杂背景下棉田杂草高效识别方法

Efficient recognition of cotton and weed in field based on Faster R-CNN by integrating FPN

为实现田间条件下快速、准确识别棉花与杂草,该文以自然光照下田间棉花与杂草为研究对象,采用垂直向下拍摄的方式获取棉花杂草视频,按1帧/s的速率从视频中提取图像,在人工去除冗余度过多的图片后,建立1000幅图片的数据集。对比了Faster R-CNN和YOLOv32种典型卷积神经网络,将Faster R-CNN卷积神经网络的深度学习模型引入到棉花杂草图像识别中,并提出一种结构优化的方法,使之适用于复杂背景下的棉田杂草识别。该文选用残差卷积网络提取图像特征,Max-pooling为下采样方法,RPN网络中引入特征金字塔网络生成目标候选框,对卷积神经网络结构进行优化。在使用700幅图片进行训练后,通过200幅田间棉花杂草图像识别测试,结果表明:该方法的平均目标识别准确率达95.5%,识别单幅图像的平均耗时为1.51 s,采用GPU硬件加速后识别单幅图像的平均耗时缩短为0.09 s。优化后的Faster R-CNN卷积神经网络相对于YOLOv3平均正确率MAP高0.3以上。特别是对于小目标对象,其平均正确率之差接近0.6。所提方法对复杂背景下棉花杂草有较好的检测效果,可为精确除草提供参考。

Cotton(Gossypium hirsutum)is one of the most important cash crops in China,The timely and effective removal of weeds in cotton seedling stage is an important measure to ensure high and stable yield of cotton.Nowadays,weed recognition based on machine vision is widely used.The fast and effective recognition of crop and weed in the field under natural illumination is one of the key technologies for the development of intelligent mechanization weeding pattern.In the one hand,cotton and weeds have similar color feature in the field.Feature presentation of the natural property of target is difficult to be obtained by the hand-engineered feature extractor.The spatial consistency of the obtained features is not good,and the real-time performance of recognition system is reduced for the complex feature extraction algorithm.On the other hand,the effect of image preprocessing has important influence on recognition results.In order to solve the main problems in the current research,we explored the way to improve the recognition accuracy,stability and real-time performance,and a recognition method of crop and weed based on Faster R-CNN.In this paper,cotton seedling at 2-5 leaves stages and weeding during the same stage were used as research objects under natural illumination.Weed identification from digital images taken under natural illumination at field level is still challenging in agricultural image processing applications,though a lot of research has been conducted related to this topic.To address this problem,images including cottons and weeds were taken vertically from top to bottom.A method based on Faster R-CNN convolutional neural network was proposed to identify weeds from cotton plants more accurately and quickly.The residual network was used to extract image features,with ReLU as the activation function and Max-pooling as the down-sampling method.In the region of proposal network,feature pyramid network was introduced to generate target candidate frame,and Softmax regression classifier was utilized to optimize the CNN network.The proposed methodology was implemented on 200 digital images taken under natural illumination.The experimental results demonstrated that,the average accuracy of weed identification reached 95.5%,and the average time for individual weed plant identification was 1.51 s,which was reduced to 0.09 s by using GPU.To test the efficiency of the proposed methodology,YOLOv3 method was also carried out on the same training and test datasets.The weed identification results were assessed by mean average precision and average precision.The experimental results showed that better performance was achieved by using our proposed methodology,and better identification accuracy was reached as well.This indicated that the proposed method had a good effect on weed detection under natural illumination,and it will greatly promote the development of precise weed control.