## 4 Results
### Quantitative Validation of the Results of the 3D Object Pose Detection and Estimation Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **mAP@.5 IOU** & **mAP@.75 IOU** \\ \hline LM & 99.01\% & 94.24\% \\ LM-O & 91.46\% & 72.55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy obtained by the YOLOV4 detection method in the LM and LM-O data sets. Finally, the 6 DOF pose tracking method used in this work uses the original implementation of the method by [PERSON] et al. [17]. This method was quantitatively evaluated by the authors, obtaining results that significantly improved upon those of methods such as PWP3D [30]. In addition, the validation performed showed that the 6 DOF pose tracking system is robust against occlusion and significant changes in the scale of the object, as well as dynamic changes in the lighting of the scene, among other factors. Some qualitative results of the MANTRA-implemented 6 DOF pose tracking can been seen, in Figure 9, for different 3D objects (i.e., water pump and fuel filter of an airplane motor). These images show that the system can obtain the pose of the 3D object precisely against significant changes in object scales, occlusion, and blurring. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**LM**} & \multicolumn{4}{c}{**LM-O**} \\ \cline{2-10}  & \multicolumn{2}{c}{**LINYOLO**} & \multicolumn{2}{c}{**LINEMOD**} & \multicolumn{4}{c}{**LINYOLO**} & \multicolumn{2}{c}{**LINEMOD**} \\ \cline{2-10}  & **ADD** & **Proj.2D** & **ADD** & **Proj.2D** & **ADD** & **Proj.2D** & **ADD** & **Proj.2D** \\ \hline Ape & 97.97\% & 98.62\% & 94.17\% & 94.41\% & 63.24\% & 68.11\% & 58.80\% & 62.73\% \\ Bench Vise & 99.01\% & 98.84\% & 97.94\% & 97.04\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Bowl & 96.48\% & 93.34\% & 95.13\% & 94.32\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Cam & 90.00\% & 89.67\% & 89.34\% & 89.34\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Can & 92.05\% & 95.31\% & 89.54\% & 93.06\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Cat & 99.15\% & 99.32\% & 96.52\% & 96.52\% & 63.19\% & 64.66\% & 49.18\% & 50.90\% \\ Cup & 95.72\% & 79.75\% & 91.12\% & 75.32\% & 17.42\% & 17.69\% & 12.58\% & 12.58\% \\ Driller & 88.55\% & 86.36\% & 81.73\% & 78.11\% & 36.88\% & 34.77\% & 27.81\% & 26.32\% \\ Duck & 96.73\% & 98.96\% & 95.77\% & 96.65\% & 60.28\% & 69.29\% & 54.08\% & 60.38\% \\ Box & 98.48\% & 98.16\% & 98.96\% & 98.80\% & 29.57\% & 25.72\% & 18.30\% & 17.14\% \\ Glue & 91.55\% & 88.85\% & 56.39\% & 54.67\% & 33.33\% & 27.92\% & 8.67\% & 8.30\% \\ Hole Punch & 97.00\% & 97.49\% & 74.13\% & 74.13\% & 90.05\% & 90.68\% & 59.06\% & 59.19\% \\ Iron & 96.78\% & 96.70\% & 92.79\% & 92.62\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Lamp & 92.99\% & 91.76\% & 84.59\% & 83.86\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Phone & 91.15\% & 90.82\% & 67.09\% & 66.93\% & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline Average & 94.90\% & 93.59\% & 87.01\% & 85.71\% & 49.24\% & 49.85\% & 36.06\% & 37.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the LINEMOD-based pose detection and estimation system on the LM [15] and LM-O [59] data sets using different metrics with LINYOLO or with LINEMOD. The percentage is calculated as the number of times the pose was correctly estimated with respect to the total number of images for each of the sequences. Figure 9: Qualitative results of the pose estimation method implemented in this work for different 3D models. The red color of the 3D model indicates the projection of the 3D model on the 2D image with the obtained pose.