基于Faster R-CNN的除草机器人杂草识别算法

Weed identification algorithm of weeding robot based on Faster R-CNN

针对当前除草机器人杂草识别定位不准确、实时性差等问题,提出一种基于Faster R-CNN的草坪杂草识别算法。该方法首先使用快速区域卷积神经网络(Faster R-CNN)算法训练初始化模型,然后通过在网络池化层后添加生成对抗网络(GAN)噪声层来提高网络的鲁棒性。试验结果表明,该种方法在正常拍摄的测试集图片中识别率达到97.05%,在加噪图片测试集的识别率达到95.15%,识别结果均优于传统的机器学习方法。同时,本方法具有识别速度快的特点,可用于实时检测,在园林杂草清理等方面具有应用价值。

Aiming at the inaccurate and inaccurate identification of weeding robots in current research,this paper proposes a lawn weed detection algorithm based on Faster R-CNN.The method first uses the fast region convolutional neural network(Faster R-CNN)algorithm to train the initialization model,and then increases the robustness of the network by adding generative adversarial network(GAN)noise layer after the network pooling layer.The experimental results show that the recognition rate of this method is 97.05%in the normal test image set,and is 95.35%in the noise-added picture test set.The detection results are better than the traditional machine learning method,and the recognition speed is fast.The features can be used for real-time detection and have application value in garden weed cleaning.