Existing single-stage target detection algorithms are insensitive to nodule detection in lung nodule detection， multiple up-samplings during feature extraction by Convolutional Neural Network （CNN） has difficult feature extraction and poor detection effect， and the existing pulmonary nodule detection algorithm models are complex and not conductive to practical application employment and implementation. To address the above problems， a real-time pulmonary nodule detection algorithm combining attention mechanism and multipath fusion was proposed， based on which the up-sampling algorithm was improved to effectively increase the detection accuracy of lung nodules and speed of model inference， the model size was small and easy to deploy. Firstly， the hybrid attention mechanism of channel and space was fused in the backbone network part of feature extraction. Secondly， the sampling algorithm was improved to enhance the quality of generated feature maps. Finally， the channels were established between different paths in the enhanced feature extraction network part to achieve the fusion of deep and shallow features， so the semantic and location information at different scales was fused. Experimental results on LUNA16 dataset show that， compared to the original YOLOv5s algorithm， the proposed algorithm achieves an improvement of 9.5， 6.9， and 8.7 percentage points in precision， recall， and average precision， respectively， with a frame rate of 131.6 frames/s， and a model weight file of only 14.2 MB， demonstrating that the proposed algorithm can detect lung nodules in real time with much higher accuracy than existing single-stage detection algorithms such as YOLOv3 and YOLOv8.