基于双维度注意力机制的西红柿成熟度检测方法

西红柿的成熟度对于采摘、运输和销售至关重要。针对西红柿需要在特定环境下识别问题，从种植现场拍摄图片来制作数据集，提出了一种基于双维度注意力机制的西红柿成熟度分类方法。首先通过tensorflow搭建卷积神经网络，网络中加入了改进的CBAM(convolutional block attention module)模块提取西红柿的成熟度和所在位置信息，即在通道注意力模块中并行一个共享多层感知器后的平均池化层；然后使用Adam优化器更新参数，此方法不仅缓解了网络中直接加入CBAM模块出现的不稳定问题，而且加快了损失函数的下降速度；最后通过调节学习率并使用混淆矩阵计算验证集的准确率获取最佳模型。实验结果表明，本文所提网络在训练了30个Epoch后损失函数稳定下降，搭建软件测试平台进行测试后得到准确率为99%,单张图片检测时间为1.5 s。检测时间和测试准确率均优于AlexNet网络，Grad-CAM可视化结果显示本文所提网络提取目标信息的效果优于AlexNet网络和改进之前的CBAM模块。本文所提方法适用于任意背景下的瓜果品级分类。

Tomato maturity detection based on two-dimensional attention mechanism

The maturity of tomatoes is crucial for picking, transportation and marketing. For the problem that tomatoes need to be identified in a specific environment. This study took pictures from the planting site to make the data set, A network model is built through tensorflow to classify the maturity of three kinds of tomatoes, Firstly, an improved CBAM module is added to the network to extract the information of tomato maturity and location, That is, in the channel attention module, an average pool layer after sharing multi-layer perceptron is paralleled, This method alleviates the instability problem of adding CBAM module directly to the network, Then use the Adam optimizer to update the parameters to speed up the decline of the loss function, Finally, by adjusting the learning rate and using the confusion matrix to calculate the accuracy of the verification set, the best model is obtained. The experimental results show that the loss function of the proposed network decreases steadily after training 30 epochs. A software test platform is built for testing, the accuracy is 99%, and the detection time of a single picture is 1.5s. The detection time and accuracy are better than that of alexnet network, The visualization results of Grad-CAM show that the effect of extracting target information by the network proposed in this paper is better than that of AlexNet network and CBAM module before improvement. The method proposed in this paper is suitable for melon and fruit grade classification under any background.