Deep convolutional neural networks have achived remarkable progress on computer vision tasks over last years. These novel neural architecture are most designed manually by human experts, which is a time-consuming process and not the best solution. Hence neural architecture search (NAS) has become a hot research topic for the design of neural architecture. In this paper, we propose the dynamic receptive field (DRF) operation and measurable dense residual connections (DRC) in search space for designing efficient networks, i.e., DRENet. The search method can be deployed on the MobileNetV2-based search space. The experimental results on CIFAR10/100, SVHN, CUB-200-2011, ImageNet and COCO benchmark datasets and an application example in a railway intelligent surveillance system demonstrate the effectiveness of our scheme, which achieves superior performance.

在过去几年中，深度卷积神经网络在计算机视觉任务方面取得了显著进展。这些新颖的神经结构大多是由人类专家手动设计的，这是一个耗时的过程，并不是最好的解决方案。因此，神经结构搜索 （NAS） 已成为神经结构设计的热门研究课题。在本文中，我们提出了搜索空间中的动态感受野 （DRF） 操作和可测量的密集残差连接 （DRC），以设计高效的网络，即 DRENet。搜索方法可以部署在基于 MobileNetV2 的搜索空间上。在 CIFAR10/100、SVHN、CUB-200-2011、ImageNet 和 COCO 基准数据集上的实验结果以及在铁路智能监控系统中的应用实例证明了我们的方案的有效性，取得了卓越的性能。