Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing (MEC) system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection and localization are important. UAV swarms show characteristics of the high density, small radar cross section (RCS), far range and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this paper, the accurate detection and localization of UAV swarms are investigated and an effective method is proposed based on the Dechirp-keystone transform (KT) and frequency-selective reweighted trace minimization (FS-RWTM). It inherits high robustness of the long-time integration (LTI) technique and super resolution of the gridless sparse technique. Mathematical analyses and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.