Aiming at the problems such as small and medium-sized obstacles on the road are prone to miss detection, small target obstacles are difficult to detect, and the number of model parameters is large in smart driving scenarios, the obstacle target detection algorithm with improved YOLOv8n is proposed. Distribution Shifting Convolution (DSConv) is incorporated into the backbone network, replacing floating-point operations with integer operations to reduce the amount of redundant computation, and mimicking the original convolutional layer by quantization and distribution shifting to maintain the accuracy; the feature information of small targets is better captured by adding a small-target detection layer to adapting the scale features of small targets; introducing the SPPF-SimAM module by combining the SimAM parameter-free attention mechanism to improve the quality and diversity of feature representations, and realizing the improvement of detection accuracy without increasing the number of parameters; and improving the detection accuracy by combining the Ghost-Shuffle Convolution (GSConv) and VoV-GSCSP module to lighten the neck feature fusion network, which reduces the number of parameters and computation of the model. The experimental results show that the accuracy, recall, and mean average precision of the improved model are improved by 1.6%, 8.0%, and 6.2%, respectively, and the number of parameters is reduced by 6.7% compared with the original model, and the proposed algorithm effectively improves the detection accuracy of small and medium-sized obstacles in smart driving scenarios, and achieves a better balance between the detection performance and the model lightweighting.