Abstract: With the development of artificial intelligence technology, vision-based object recognition, as an important component of the Internet of Things perception layer, can be applied in various cargo sorting scenarios when combined with sorting robots. However, in complex environments where objects are disorganized, relying solely on object recognition algorithms may not be sufficient to distinguish target objects from interfering ones, as the difference between them may be the spatial distance. This can negatively impact the robot’s normal grasping actions. To address this issue, we propose a lightweight object recognition method for complex environments—3Deus. This method combines deep learning with 3D point cloud data processing. It uses a depth camera to synchronously capture both RGB images and 3D point cloud data in the recognition scene. The method eliminates the plane where interfering objects are located using the 3D point cloud, and distinguishes the plane containing the target object from the stacked interfering objects with an improved RANSAC algorithm. It also employs a plane expansion mechanism to ensure the integrity of the plane point cloud where the target object is located. Finally, the object recognition algorithm is used to obtain the recognition bounding box of the target object and construct its mapping relationship with the specific point cloud cluster. We deploy 3Deus on edge computing devices and validate its effectiveness and robustness by constructing different experimental scenarios. Experimental results show that compared with the traditional random sampling consensus algorithm, 3Deus can effectively distinguish the target objects from interference objects, achieving an accuracy of over 95%. Moreover, by utilizing the plane expansion mechanism, the recognition accuracy of the target object is improved by 30.48%.