## 5 Observation and Analysis  
Since 2017, most of the 3D object detection methods have been implemented for autonomous vehicle navigation. Numerous competitions have been organized on different open benchmarks, especially on road scenes. Three-dimensional object detection is gradually becoming popular in other domains as well, with the availability of GPU-based computers allowing high computation capabilities. A comparison of the common devices for 3D detection is presented in Table 5. Information in this table is collected from the research work of [PERSON] et al. (2021) [132]. The performance of 3D object detection methods depending on the type of sensor can be seen from Table 6. Table 6 contains the top average precision methods exhibited by different modalities in the KITTI vision benchmark leaderboard. Figure 4: (**a**) True Positive; (**b**) False Positive; (**c**) True Negative; (**d**) False Negative (the red rectangle indicates ground truth, and the green rectangle stands for detection). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Performance Criterion** & **RADAR** & **LiDAR** & **RGB Camera** \\ \hline Object detection & Good & Good & Fair \\ Object classification & Poor & Fair & Good \\ Distance inference & Good & Good & Fair \\ Detecting edge & Poor & Good & Good \\ Visibility range & Good & Fair & Fair \\ Adverse weather performance & Good & Fair & Poor \\ Performance in low light condition & Good & Good & Fair \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of 3D object detection sensors and devices. Table 6 shows that LiDAR-based methods exhibited maximum average precision. Multimodal techniques are a recent addition; in particular, LiDAR with camera techniques is showing good potential in terms of precision value. Thin surface objects (such as pedestrians) detection achieved average precision than wide surface cars. The monocular techniques captured only RGB image frames and then the images were trained in sequential network (e.g., LSTM). For dealing with only RGB, the speed was found to be fast, but the average precision was very much less than stereo or LiDAR. Analyzing the practiced modalities of 3D object detections, following SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis, Tables 7-9 are developed. \begin{table} \begin{tabular}{l l l l l l l l} \hline \hline
**Modality** & **Method** & **Reference** & **Category** & **Easy** & **Moderate** & **Hard** & **Run Time** \\ \hline LiDAR+ RGB & VirConv-S & [133] & Car & 92.48\% & 87.20\% & 82.45\% & 0.09 s \\ LiDAR+ RGB & LoGNet & [134] & Car & 91.80\% & 85.06\% & 80.74\% & 0.1 s \\ LiDAR+ RGB & LoGNet & [134] & Pedestrian & 54.04\% & 47.43\% & 44.56\% & 0.1 s \\ LiDAR & CasA++ & [135] & Car & 90.68\% & 84.04\% & 79.69\% & 0.1 s \\ LiDAR & CasA++ & [135] & Pedestrian & 56.33\% & 49.29\% & 46.70\% & 0.1 s \\ Stereo & DSGN++ & [58] & Car & 83.21\% & 67.37\% & 59.91\% & 0.2 s \\ Stereo & DSGN++ & [58] & Pedestrian & 43.05\% & 32.74\% & 29.54\% & 0.2 s \\ Stereo & DMF & [136] & Car & 77.55\% & 67.33\% & 62.44\% & 0.2 s \\ Stereo & DMF & [136] & Pedestrian & 37.21\% & 29.77\% & 27.62\% & 0.2 s \\ Monocular & CIE + DM3D & [137] & Car & 35.96\% & 25.02\% & 21.47\% & 0.1 s \\ (RGB) & QD-3 DT & [138] & Car & 12.81\% & 9.33\% & 7.86\% & 0.03 s \\ (RGB) & [LSTM on RGB] & [138] & Pedestrian & 5.53\% & 3.37\% & 3.02\% & 0.03 s \\ Monocular & QD-3 DT & [138] & \multicolumn{1}{c}{} & & & & \\ (RGB) & [LSTM on RGB] & [138] & \multicolumn{1}{c}{} & & & & \\ (LSTM on RGB) & [138] & \multicolumn{1}{c}{} & & & & \\ (LSTM on RGB) & [138] & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}