Streamer discharges determine the very first stage of sparks or lightning, and they govern the evolution of huge sprite discharges above thunderclouds as well as the operation of corona reactors in plasma technology. Streamers are nonlinear structures with multiple inner scales. After briefly reviewing basic observations, experiments and the microphysics, we start from density models for streamers, i.e. from reaction-drift-diffusion equations for charged-particle densities coupled to the Poisson equation of electrostatics, and focus on derivation and solution of moving boundary approximations for the density models. We recall that so-called negative streamers are linearly stable against branching (and we conjecture this for positive streamers as well), and that streamer groups in two dimensions are well approximated by the classical Saffman-Taylor finger of two fluid flow. We draw conclusions on streamer physics, and we identify open problems in the moving boundary approximations.

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Achieving accurate and efficient detection of road damage in complex scenes has always been a challenging task. In this paper, an enhanced lightweight network, E-EfficientDet, is proposed. Firstly, a feature extraction enhancement module (FEEM) is designed to increase the receptive field and improve the feature expression capability of the network, which can extract richer multi-scale feature information. Secondly, to promote the reuse of feature information between different layers in the network and take full advantage of multi-scale context information, four pyramid modules with different structures are designed based on the idea of semi-dense connection, among which the bidirectional feature pyramid network with longitudinal connection (LC-BiFPN) is more suitable for road damage detection. Finally, to meet the road damage detection tasks under different hardware resource constraints, the E-EfficientDet-D0~D2 networks are proposed in this paper based on the compound scaling strategy. Experimental results show that the detection accuracy of E-EfficientDet-D0 improves by 2.41% compared with the original EfficientDet-D0 on the publicly available road damage dataset and outperforms other networks such as YOLOv5s, YOLOv7-tiny, YOLOv4-tiny, Faster R-CNN, and SSD. Meanwhile, the detection speed of EfficientDet-D0 can reach 27.0 FPS, which meets the demand for real-time detection, and the model size is only 32.31 MB, which is suitable for deployment in mobile devices such as unmanned inspection carts, UAVs, and smartphones. In addition, the detection accuracy of E-EfficientDet-D2 can reach 57.51%, which is 4.39% higher than E-EfficientDet-D0, and the model size is 61.78 MB, which is suitable for practical application scenarios that require higher detection accuracy and better hardware performance.

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