Detection of underwater obstacles represents a significant area of interest within marine detection technologies. Airborne LiDAR, an active detection modality, has found extensive application in domains including terrain surveying and underwater obstacle detection. The streak tube camera, by splitting laser light, considerably reduces the energy of emitted laser pulses for each channel. In contrast, single beam LiDAR detection capitalizes on the full energy of the emission pulse, facilitating deeper detection capabilities. LiDAR is particularly effective for detecting underwater obstacles in turbid water conditions. Considering the data characteristics of line scanning airborne LiDAR and the detection requirements, this study introduces an image processing approach inspired by the streak tube imaging system. The method involves splicing echo waveform data from each point on a scanning line to create a two-dimensional profile that directly illustrates the spatial distribution of echo energy, from which obstacle information is subsequently extracted and analyzed. An automated obstacle identification criterion is developed and validated. This research contributes to the refinement of data processing methods for underwater obstacle detection and identification using airborne LiDAR systems.A sequence of LiDAR echo data strips is chronologically assembled; Each column corresponds to an echo waveform, with gray values representing the echo energy at each point. Rows, ordered from top to bottom, depict the amplitude at respective sampling moments. To address horizontal plane deformation due to scanning angle variations, the water surface slope distance is initially extracted from the waveform using the Linear Leading Edge Approximation (LLE) method. A model representing the emission angle of the laser light is then developed, based on the scanning architecture of the ocean LiDAR system. These components are integrated to pinpoint water surface points, facilitating the calculation of the discrepancy between the laser slope distance and the actual height at specific angles, thereby correcting water surface deformation and enabling accurate obstacle contour restoration. In the subsequent image processing phase, the Canny edge detection operator is employed to identify edges with high echo energy in images generated by adjacent scan lines. This analysis includes evaluating the depth position consistency via the centroid of edge pixels and comparing contour shapes using Hu moments. Ultimately, an automatic obstacle identification criterion is established and validated using 20 sample images to assess its efficacy.The integration of angular modeling with Linear Leading Edge (LLE) extraction for slope distance demonstrates notable corrective effects. Energy profiles pre- and post-correction are illustrated in Fig.7(a) and 7(b), respectively. Prior to correction, the water surface height exhibits significant undulation, characterized by an increase in slope distance at larger scanning angles. Post-correction, the water surface along a scanning line appears flatter, aligning with experimental observations on a calm lake, as seen in Fig.8. Post-correction, the water surface height fluctuates around 0.45 m. Concerning obstacle morphology, the results post-correction, as shown in Fig.9, markedly improve over those in Fig.4. A high degree of similarity exists between obstacle contours extracted from adjacent scan lines through image processing, detailed in Tab.1. The maximum depth difference across six images is 0.092 4 m, indicating that the variation in depth for the center of gravity of the extracted obstacle contours remains below the system's vertical detection resolution. Automatic identification successfully detects obstacles in 17 out of 19 images, achieving a detection success rate of 89.5%, with representative samples depicted in Fig.12.LiDAR echo data are integrated into two-dimensional images, followed by angular correction to restore the morphology of underwater obstacles. Contours are subsequently extracted using advanced image processing techniques. The algorithm's effectiveness is evaluated through comprehensive correlation analysis, leading to the establishment of a recognition criterion for validating automatic detection. This approach provides a valuable reference for the development of data processing methods in underwater obstacle detection and recognition using airborne LiDAR systems.