To avoid the danger of people using mobile phones while walking, this study proposes a lightweight model (Mobile-YOLOv3) with strong real-time performance to detect road obstacles. We photograph roadblocks and annotate a roadblock data set around Guangzhou City. Lightweight is achieved by the replacement of the backbone network of YOLOv3 with a lightweight MobileNetv1 network. In addition, we apply four methods to improve detection accuracy and model robustness, i.e., border regression loss function CIOU, classification loss function Focal, prediction box screening algorithm Soft-NMS, and negative sample training. The experimental results show that the model obtains 98.84% MAP. Compared with YOLOv3, this model has the scale reduced by 2.5 times but the detection accuracy improved by 7%.