基于轻量型卷积神经网络的交通标志识别方法

交通标志识别设备的功耗和硬件性能较低,而现有卷积神经网络模型内存占用高、训练速度慢、计算开销大,无法应用于识别设备.针对此问题,为降低模型存储,提升训练速度,引入深度可分离卷积和混洗分组卷积并与极限学习机相结合,提出两种轻量型卷积神经网络模型:DSC-ELM模型和SGC-ELM模型.模型使用轻量化卷积神经网络提取特征后,将特征送入极限学习机进行分类,解决了卷积神经网络全连接层参数训练慢的问题.新模型结合了轻量型卷积神经网络模型内存占用低、提取特征质量好以及ELM的泛化性好、训练速度快的优点.实验结果表明.与其他模型相比,该混合模型能够更加快速准确地完成交通标志识别任务.

Traffic Sign Recognition Method Based on Lightweight Convolutional Neural Network

Traffic sign recognition equipment has low power consumption and hardware performance, while the existing convolutional neural network model has high memory footprint, slow training speed, and high computational overhead, which cannot be applied to the recognition equipment. To solve this problem, in order to reduce model storage and improve training speed, deep separation convolution and mixed wash grouping convolution are introduced and combined with the ultimate learning machine. Two lightweight convolutional neural network models are proposed:DSC-ELM model and SGC-ELM model. The proposed models use the lightweight convolutional neural network to extract the features, and then send the features to the extreme learning machine for classification, which solve the problem of slow parameter training in the full connection layer of the convolutional neural network. The new models combine the advantages of lightweight convolutional neural network model with low memory footprint, good feature extraction quality, good generalization of ELM, and fast training and classification. Experimental results show that compared with other models, the hybrid model can accomplish traffic sign recognition tasks more quickly and accurately.