Due to the various types of logistics pallets, complex shape rules, and the problems of pallets being blocked and changing light conditions in industrial production environment, a novel U-Net network based on squeeze excitation dilated convolution (SEDC) was proposed. By modeling the correlation between feature channels, the important features were strengthened and the segmentation performance of logistics pallet images was improved. Specifically, 1×1×1 convolution in the SEDC module was used for data dimensionality reduction and dimensionality upgrade, which greatly reduced the amount of calculation, and image features were effectively explored under different fields of view through normal convolution and hole convolution with an expansion rate of 2, while automatically learning the importance of different layers through the SE module. Experimental results showed that compared with some existing classical image segmentation algorithms, the proposed model greatly reduced the computational burden and improved the robustness of the network while ensuring the performance of image segmentation as much as possible, and was expected to provide a new solution for intelligent segmentation of logistics pallet images.