In wild scenes, the detection of license plates (LP) is a fundamental task in realistic intelligent transportation applications. However, most existing license plates detection models are not designed for license plate detection task specifically. As a standard single target object detection task, just adapting the multiple detection method to licence plate detection may not be a good choice. Hence, in this paper, we choose a single detection method of state of the art RetinaNet which works well in unconstrained environment. By adding the attention module to the Neural Networks, we choose a more proper bbox loss function of CIoU. In the experiments, we test and evaluate on the largest Chinese License Plate dataset CCPD. Experimental results show that our proposed method can achieve better performance with very high accuracy of 99.75%, compared with other methods.