Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano.

中文翻译：

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CLMIP：基于跨层流形不变性的深度卷积神经网络剪枝方法，用于实时道路类型识别

最近，基于深度学习的模型已经证明了在各种视觉任务（如对象检测和实例分割）中的优越性。在实际应用中，由于昂贵的计算成本和内存占用，将高级网络部署到自动驾驶等实时应用中仍然具有挑战性。在本文中，为了减小深度卷积神经网络 (CNN) 的大小并加速其推理，我们提出了一种名为 CLMIP 的基于跨层流形不变性的剪枝方法，用于网络压缩，以帮助其在低密度下完成实时道路类型识别。成本视觉系统。流形是曲线和曲面的高维类似物，可以自组织以反映数据分布并表征数据之间的关系。所以，我们希望通过保持网络中各层数据流形的一致性来保证深度CNN的泛化能力，然后去除对流形结构影响较小的参数。因此，CLMIP 可以看作是进一步研究模型结构对网络优化和泛化的依赖性的工具。据我们所知，这是第一次基于数据流形的不变性修剪深度 CNN。在实验过程中，我们使用基于python的关键字爬虫程序收集了102个车载摄像头的第一视角视频，包括4个道路场景（城市道路、越野、主干道路和高速公路）的137 200张图像（320×240）。最后，