In natural scenes, logos such as trademarks and traffic signs are susceptible to shooting angle, carrier deformation, and scale changes, which reduces logo detection accuracy. Thus, this study proposes an attention guided logo detection and recognition network (AGLDN) to jointly optimize the model robustness for multi-scale and complex deformation. First, a logo synthesis dataset is established by image collection and mask generation of logo templates, image selection of logo background, and logo image generation. Then, based on RetinaNet and FPN, multi-scale features are extracted and high-level semantic feature mapping is formed. Finally, the attention mechanism guided network is employed to focus on the logo area, and the influence of logo deformation on feature robustness is suppressed to improve logo detection and recognition. Experimental results show that the proposed method can reduce the influence of scale changes and non-rigid deformation, and improve detection accuracy.