## 4 Results
### Quantitative Validation of the Results of the 3D Object Pose Detection and Estimation Method  
To quantitatively evaluate the influence of the incorporation of the YOLOV4 algorithm to the detection and estimation method of the initial pose of 3D objects, the LINEMOD (LM) [15] and LINEMOD-OCCLUDED (LM-O) [59] data sets were used. On one hand, the LM data set consists of 15 non-textured 3D objects, for which 15 color 3D models are available. For each of the models, a sequence of RGB-D test images in which the object of interest appears in a cluttered environment with the real pose annotated (\"ground truth\") is available. On the other hand, the LM-O data set consists of 8 objects extracted from the LM data set, in which these 8 models appear simultaneously, with different levels of occlusion and where the actual pose of each object has also been annotated. To evaluate the performance of the method, the average distance of the model points (ADD) [15] and the 2D projection error (Proj.2D) [60] were used as metrics. The pose is generally considered correct if the ADD is less than 10% of the object's diameter while Proj.2D metric considers that a pose is correct if it is less than 5 pixels. Before performing the quantitative validation of the initial pose estimation method, the precision of the YOLOV4 detection algorithm was validated on both the LM and LM-O data sets, as the precision of this directly influences the object pose estimation method. For this, 50,000 synthetic images were generated using BlenderProEp Section 3.2.3 for training the YOLOV4 algorithm, in which the different LM objects appear (at least 8 LM different models), as well as distractors from other data sets. This algorithm was tested against the LM and LM-O RGB-D real test images. The results obtained, as measured with the mAP@.5 IOU and mAP@.75 IOU metrics, are shown in Table 3.  
In Table 4, the LINEMOD and LINEMOLO pose estimation results on the LM and LM-O data sets can be seen. It is important to highlight that unlike the work of [15], the same parameters were used for all the objects to estimate the pose; specifically, \(\epsilon_{s}=80\) was used as the similarity score of the LINEMOD method. Also, as post-processing, the ICP algorithm was only performed on the four most similar templates; using, in this case, the same parameters for the ICP algorithm in all models. Taking into account the results obtained (see Table 4), it can be seen that the incorporation of the YOLOV4 detection algorithm improved the results by 7.89% when using ADD as a metric and by 7.88% when using Proj.2D, in terms of the results obtained by the pose estimation method in the LM data set. This difference was even more significant in the LM-O data set, the improvement in this case being 13.18% using ADD metric. This indicates that the use of YOLOV4 improved the results of estimating the object pose, especially when occlusion occurred over the object. [PERSON] et al. [61] recently reported the results of the most significant pose estimation methods based on deep-learning techniques from the scientific literature on different data sets. Using this report as a benchmark, LINEMOLO used in this work improved upon the results of all the evaluated methods in the LM and LM-O data sets with the ADD measure, except for the DPOD method [33], which obtained an ADD of 95.2% in the LM data set. This fact indicates that LINEMOD methods is still a competitive object pose estimation method. Despite the results obtained (see Table 4) with LINEMOLO, this method still suffered when there was an occlusion. This fact is a problem as, for example, the user will need to manipulate the 3D object on many occasions and, consequently, the implemented method will not be able to estimate its pose. Therefore, to solve this and similar problems, a 6 DOF pose tracking method was incorporated into the AR system.