In order to solve the problems of low accuracy, slow detection speed and high model complexity of obstacle detection of campus intelligent sweepers, a modified YOLOv8s obstacle detection and ranging algorithm for campus intelligent sweepers YOLOv8s-FDR was proposed. On the basis of the YOLOv8s algorithm framework, the backbone network is replaced by the FasterNet network with smaller parameters and memory access, so as to reduce the complexity of the model and improve the detection speed. Then, the SPPF-DAM module was designed to introduce the deformable attention mechanism (DAM) in the form of residuals to improve the model′s perception of multi-scale target features. Secondly, Partial-RFEM was used for downsampling in the feature fusion network to capture the non-local context features and local target features to improve the detection accuracy. Finally, the ranging function is added to reduce hardware costs. Experimental results show that compared with the original algorithm, the mAP of the improved algorithm is increased by 3.6%, and the amount of model computation and parameters is reduced by 19.72% and 15.27%, respectively. The actual environment test shows that the detection speed of the YOLOv8s-FDR algorithm reaches 38.44 fps, which is much higher than the 17.12 fps of the original algorithm, which can meet the performance requirements of the normal operation of the campus intelligent sweeper.