To address challenges faced by most volume measurement methods, such as difficulties in transferring, poor real-time performance, and sparse, noisy, and perturbed Lidar point cloud data, this article presents a vision-based correction method for Lidar volume measurement for small ‘L’-shaped objects. The proposed method first aligns camera and Lidar data through joint calibration and time-stamped nearest-neighbor matching. Subsequently, it leverages target detection algorithms to extract information from images while simultaneously performing ground segmentation on point cloud data to distinguish ground and non-ground points. By employing visual projection and point cloud clustering, the method segments target point clouds and utilizes KDtree to identify ground points in proximity to the target point cloud. Finally, a 3D box fitting algorithm is proposed to provide initial rough estimation of the point cloud target′s 3D box. A visual correction model is established to refine the target′s 3D box, and enable accurate volume calculation. Experimental results show that for ‘L’-shaped objects like weapon crates, medical boxes, and barrels, the proposed algorithm achieves promising results within a certain range. The average relative error in volume measurement is less than 4. 44% , with a maximum error below 6. 12% and a maximum repeatability error of 5. 61% . In addition, the integration of the visual correction model significantly enhances the algorithm′ s accuracy and stability. The processing of frame on an embedded platform takes 55 ms, demonstrating the capability to achieve realtime, high-precision volume measurement. This method holds great promise for practical engineering applications.