As light detection and ranging (LiDAR) develops, the extraction of forest structure parameters has been one of hot topics in related fields in the past years. However, the accuracy of detection is the key factor in obtaining the forest individual tree parameters. The individual tree detection methods can be divided to two types: one is based on the canopy height model (CHM) and the other is based on the point cloud distribution. We can identify an individual tree by using the method of the crown boundary segmentation. Also, we can identify the tree top by local maximum algorithms and then perform the regional growth or image segmentation. Based on the point cloud distribution, the canopy is identified by region growing or clustering algorithms in three-dimensional space. We analyze the advantages and disadvantages of different individual tree detection methods in terms of precision of individual tree detection, and compare their effects on omission errors and commission errors in different regions. The factors influencing the precision of data such as data type, point cloud density, season and tree growth status are discussed. It is found that the accuracy of the full-waveform data is higher than that of discrete-echo data. The density of the point cloud data of 10 pt/m2 can meet the individual tree detection requirement. The accuracy of data obtained in winter is higher than that in summer. The limitation of airborne LiDAR data and its shortcomings in individual tree detection are discussed. In the end, the future directions of individual tree detection are described, from the aspects of data acquisition type, data acquisition time, data organization and management, multi-source data fusion, comprehensive application of multi-detection algorithms, and machine learning increasing the training set to find the optimal model, to help with the research and management of forest and related fields.