计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211200009-6.doi: 10.11896/jsjkx.211200009
邓朋飞, 官铮, 王宇阳, 王学
DENG Peng-fei, GUAN Zheng, WANG Yu-yang, WANG Xue
摘要: 传统的图像识别方法在对玉米病害图像进行识别时准确率低,而卷积神经网络对图像识别有很好的效果,但其网络模型计算量大、参数量多,使其难以依托算力有限的移动端设备在小样本应用中推广使用。因此,以提高玉米病害图像的准确率、降低网络参数和模型大小为目的,提出了一种结合迁移学习和模型压缩的卷积神经网络用于玉米病害识别。为提高模型的泛化性,对数据集进行增强,构建基于迁移学习的卷积神经网络结构。通过迁移学习,利用在ImageNet上预先训练改进的VGG16-Inception网络模型,对常见玉米病害图像进行迁移识别。实验表明,在ImageNet数据集上,利用迁移学习对玉米病害图像的平均识别准确率达到93.38%。在迁移完成后,结合通道剪枝和知识蒸馏的方法对模型进行压缩,压缩后的模型再利用迁移学习进行玉米病害图像识别。实验表明:压缩后对玉米病害图像的平均识别准确率达到92.40%,准确率下降了0.98%,模型大小由73.90 MB压缩到9.45 MB,参数量减少了87.80%。本方法能够在小样本场景下确保识别准确率,并进一步实现模型轻量化。
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