In order to solve the problem of complex and diverse warehouse environment and the low performance of traditional warehouse object detection models, this paper proposes an improved YOLOv5 (You Only Look Once version 5) warehouse object detection model YOLOv5-CE (YOLOv5-ConvNeXt EIoU) which based on the PaddlePaddle framework. Firstly, to improve the detection of warehouse objects in complex and diverse environments, the ConvNeXt network is used to replace the original YOLOv5 backbone network to improve the feature extraction ability of small and medium-sized warehouse objects. Secondly, in order to improve the convergence speed of the model and the detection accuracy of objects, EIoU Loss (efficient intersection over union loss) is used to replace the loss function of the original model. Finally, by using the self-made warehousing training set to carry out multi-model comparison experiments. The experimental results show that when detecting cargo, tray and forklift, the average detection accuracy of the improved model (mAP@0.5:0.95, mean average precision@0.5:0.95) reaches 89.8%, which is 1.1 percentage points higher than the original YOLOv5, of which 4.2 percentage points is increased in small-scale warehousing objects; in the detection of medium and large-scale warehouse objects, it increased by 1 percentage point. The average recall rate for small warehouse objects increased from 61.1% to 66.8%. Compared with other models such as YOLOv6, YOLOX, YOLOv7, and Faster R-CNN, YOLOv5-CE all shows better accuracy. At the same time, in view of the above model, YOLOv5-CE also achieves a good balance in the number of model parameters, detection speed and detection accuracy, which can better meet the precise detection of warehouse objects of different sizes and types.