A retail commodity detection algorithm based on improved YOLOv8s is proposed in response to the difficulty in accurately extracting global features and irrelevant feature interference caused by retail commodity rotation and deformation. Firstly, using normalized deformable convolutions to replace some standard convolutions enhances the ability to extract global features by fully capturing long-range dependencies and highlighting key channel features. Secondly, using an improved dynamic detection head and a multi-attention mechanism based on spatial perception, scale perception, and task perception captures more discriminative local features of goods to suppress irrelevant feature interference. Finally, the InnerEIoU loss function is used to replace CIoU to reduce the missed detection rate of goods. Experimental results show that the proposed algorithm achieves an mAP@0.5:0.95 of 93.3% on the RPC retail commodity dataset, which is 1.5% higher than the original algorithm and better than other mainstream detection algorithms. At the same time, the number of model parameters and the amount of computation decrease by 10.0% and 6.5% respectively, enabling accurate retail commodity detection in practical scenarios with limited storage and computing resources.