To achieve accurate detection of items in the warehousing environment, a lightweight warehousing cargo detection method (E-YOLOv4-Lite) is proposed for use in intelligent warehousing robots. This technique builds on YOLOv4 by introducing the MobileNetv3 network to reconstruct the feature extraction network, replacing standard convolution in PANet with deep separable convolution, and reducing the number of model parameters and processing. The improved convolutional block attention module (CBAM) is integrated to improve network detection performance further. In the channel attention module, the improved CBAM replaces the full connection layer with adaptive one-dimensional convolution, and in the spatial attention module, the residual structure with expansive convolution is used to expand the receptive field. Finally, the network training and experimental tests are conducted through the RPC commodity data set, the number of parameters is 11.25 MB, the detection time is 14.4 ms, the frames per second is 69.2, and the mean average precision is 95.43%. The experimental results reveal that the improved E-YOLOv4-Lite model has the advantages of high accuracy, good real-time performance, and lightweight, allowing it to better meet the needs of cargo detection in storage environments.