基于轻量型残差网络的自然场景水稻害虫识别

准确识别水稻害虫对水稻及时采取防护和治理措施具有重要意义，该研究以自然场景中水稻害虫图像为研究对象，针对水稻害虫图像的颜色纹理与背景相近以及同类害虫形态差异较大等特点，设计了一个由特征提取、全局优化以及局部优化模块构成的轻量型残差网络（Light Weight Residual Network，LW-ResNet）用于水稻害虫识别。在特征提取模块通过增加卷积层数以及分支数对残差块进行改进，有效提取自然场景中水稻害虫图像的深层全局特征并使用全局优化模块进行优化；局部优化模块通过设计轻量型注意力子模块关注害虫的局部判别性特征。LW-ResNet网络在特征提取模块减少了残差块的数量，在注意力子模块中采用深度可分离卷积减少了浮点运算量，从而实现了模型的轻量化。试验结果表明，所设计的LW-ResNet网络在13类水稻害虫图像的测试数据集上达到了92.5%的识别准确率，高于VGG16、ResNet、AlexNet等经典卷积神经网络模型，并且LW-ResNet网络的参数量仅为1.62×106个，浮点运算量仅为0.34×109次，低于MobileNetV3轻量级卷积神经网络模型。该研究成果可用于移动端水稻害虫的自动识别。

Rice pest identification in natural scene based on lightweight residual network

Abstract: Accurate identification of rice pests is of great significance for timely protection and management of rice. However, the rice pests are similar with the background in the color and texture, and the morphology of the pests varies greatly during different growth stages. Therefore, it is difficult to accurately identify the rice pests in natural scenes. In this paper, the Light Weight Residual Network (LW-ResNet) composed of feature extraction, global optimization and local optimization modules was designed to improve the ability to identify rice pests in natural scene images. Firstly, in order to reduce the influence of complex background and enhance the feature extraction and expression capabilities of the residual network, the residual block is improved to constitute the feature extraction module. The improved residual block increases the number of convolutional layers and branches of the original residual block, which can effectively extract the deep global features of rice pest images. Secondly, the deep global features are further optimized through the convolutional layers in the global optimization module. Finally, in order to obtain the local discriminative characteristics of rice pest images to distinguish the morphological differences between similar pests, the lightweight attention sub-module constitutes the local optimization module. The light weight attention sub-module uses depth separable convolution to reduce the redundancy of channel features and realize the aggregation of different channel characteristics, so it can highlight the local key features of rice pests. Because the improvement of the residual block in the feature extraction module reduces the number of residual blocks, and the use of deep separable convolution in the attention sub-module and the channel-based global average pooling and global maximum pooling encoding operations reduce floating point operations, the LW-ResNet network has achieved lighter weight. In the HSV space, Gamma transform is used to preprocess the v component of rice pest images and then proceed to the data expansion. After the expansion, there are 4 380 images in the training set and 492 images in the test set. In order to verify the rationality and effectiveness of the method in this paper, in the training phase, the cosine learning rate decay strategy was used to train the network model. By analyzing the number of the improved residual blocks in the feature extraction module, the lightweight attention sub-module in the local optimization module, and the global optimization module, the rationality of the method in this paper was verified. In the testing phase, the LW-ResNet network model achieves a identification accuracy of 92.5% on the test data set of 13 types of rice pest images. The identification accuracy of the LW-ResNet network model is higher than that of classic convolutional neural network models such as VGG16, ResNet, and AlexNet. The parameter amount of the LW-ResNet model is 1.62×106, and the amount of floating-point operations is 0.34×109. The number of parameters and floating-point operations of the LW-ResNet model are both lower than those of MobileNetV3, which verified the effectiveness of the method in this paper. The LW-ResNet network model has achieved light weight and a good identification effect, so it can be used for rice pest identification on the mobile terminal.