Aiming at the current problems of insufficient obstacle detection accuracy, slow detection speed, large number of model parameters and poor detection of small target obstacles in the complex environment of urban roads, an improved YOLOv8n lightweight urban driving road obstacle detection algorithm is proposed. Firstly, the MRObstacle urban road obstacle target detection dataset is produced to extend the types and numbers of obstacle detection; secondly, a new SPS_C2f backbone network is designed to improve the backbone network, to reduce the number of network parameters and to improve the detection speed, and the M_ECA attention module is added to the Neck portion of the network, to improve the network detection speed and the feature expression ability; thirdly, the BiFPN is integrated with a feature pyramid and a small target detection algorithm is added to the network. feature pyramid and adding a small target detection head to better capture the features of small-sized obstacles; finally, using the loss function MPDIoU that optimises the values of the bounding box width and height to improve the performance of the network bounding box regression. Compared with the original YOLOv8n algorithm, the mAP0.5 metric of this algorithm is improved by 2.04% to 97.12%, the FPS value is improved by 12.08 frames per second (fps) to 107.45 fps, and the volume of the network parameter is reduced by 10% to 2.73 MB.This algorithm improves the detection accuracy and speed while decreasing the number of parameters, and it can be better applied to the urban road obstacle detection task.