Recently, deep learning has introduced new prospects to numerous practical applications such as image recognition, robot navigation, gene engineering, language processing, and traffic sign identification. Several network models including AlexNet, VGGNet, GoogLenet and ResNet, have achieved milestone contributions while relying on massive computing resources. However, when faced with a small number of labeled examples, especially in the case of unbalanced datasets, the cumulative error and time-consuming convergence reduce their efficacy. Inspired by the convolutional output layer with a [Formula: see text] kernel, a convolutional nonlinear transfer approach with partial cooperating (CNN-COL) is proposed to address this challenge. Meanwhile, a novel method for data augmented balance can enhance the influence of small and unbalanced samples in the CNN-COL. Related experiments show that the proposed CNN-COL can effectively improve the quality of a dataset and achieve superior performance with respect to traffic sign identification based on a small and type-unbalanced dataset.

中文翻译：

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在卷积神经网络中使用部分合作策略进行交通标志识别

最近，深度学习为图像识别、机器人导航、基因工程、语言处理和交通标志识别等众多实际应用带来了新的前景。包括 AlexNet、VGGNet、GoogLenet 和 ResNet 在内的多个网络模型在依赖海量计算资源的同时取得了里程碑式的贡献。然而，当面对少量标记示例时，尤其是在数据集不平衡的情况下，累积误差和耗时的收敛会降低其功效。受具有 [公式：见文本] 内核的卷积输出层的启发，提出了一种具有部分合作 (CNN-COL) 的卷积非线性传输方法来应对这一挑战。同时，一种新的数据增强平衡方法可以增强 CNN-COL 中小样本和不平衡样本的影响。相关实验表明，所提出的 CNN-COL 可以有效地提高数据集的质量，并在基于小型且类型不平衡的数据集的交通标志识别方面取得优异的性能。