We propose a 3D Hough transform (3D-HT) algorithm that can overcome the disadvantages of high complexity and large data storage space of the existing 3D-HT-based small target detection algorithms. The proposed algorithm uses two coordinates at different times and coordinate errors to construct a 3D pipeline. Subsequently, it counts the number of points in the 3D pipeline and confirms the presence of a target trajectory in the pipeline when the number of points exceeds a predefined threshold. Finally, it performs trajectory merging and filtering before outputting the target trajectory coordinates. The proposed algorithm has low complexity because the used trajectory parameters are the coordinates in the data space, and only a linear transform between the coordinates is required. Unlike the existing algorithms that use an accumulator array to represent the Hough space, the proposed algorithm uses only a single position-adaptive cumulative cell in the Hough space. Therefore, there is no limitation on data storage in the Hough space. Simulation and analysis show that the small target detection algorithm based on the proposed transform is robust to noise, requires small data storage space, and has high computational efficiency. The proposed algorithm can be used in infrared and radar small target trajectory detection systems.