# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 4 Experiments Results and Discussion
### Object Segmentation and Recognition  
Firstly, the image to be segmented is inputted into the training dataset. Through data standardization, image clipping transformation, and other processing techniques, the data is propagated forward through a designed network model to obtain the segmented image. The loss between the segmented image and the target image is then calculated for backpropagation purposes. This process of updating model weights continues until a set number of iteration rounds are reached. Subsequently, the test dataset is loaded and fed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Left Camera**} & \multicolumn{2}{c}{**Right Camera**} \\ \cline{2-5}  & **X Direction** & **Y Direction** & **X Direction** & **Y Direction** \\ \hline Focus & 341.98 & 338.82 & 340.64 & 340.17 \\ \hline Distortion & \(0.15921\), \(-0.31659\), & \(0.05029\), \(-0.14436\), & \\ parameter & \(-0.00949\), \(-0.2309\), \(0\) & \(-0.01175\), \(-0.0158\), \(0\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration parameters of the binocular camera.  
Figure 5: Binocular camera.  
Figure 6: The map of the indoor location test environment.  
into the trained model for evaluation. The performance of the model is assessed using various metrics, and if it fails to meet the desired criteria, adjustments are made to the training process before saving a satisfactory version.  
In this experiment, we initially trained the network model with a standardized dataset to obtain preliminary network parameters. We further refined these parameters by training on our self-made dataset collected in an indoor environment consisting of 1000 training sets and 400 test sets. The images in this dataset include roads, tables, persons, chairs, and other items considered as background.  
The primary metric for evaluating the performance of a segmentation algorithm is the mean Average Precision (\(mAP\)). Firstly, precision and recall are calculated. Precision represents the ratio of correctly detected data to the total number of detected data, while recall represents the ratio of correctly identified samples to the total number of positive samples. Secondly, Average Precision is computed by plotting a precision-recall curve based on these two metrics and calculating the area under it.  
\[A\text{ve}P=\int_{0}^{1}p(r)dr \tag{11}\]  
The computation of the mean average precision indicator \(mAP\) is obtained by taking the average across all categories.  
\[mAP=\frac{\sum\limits_{k=1}^{N}AveP(k)}{N} \tag{12}\]  
where \(N\) represents the number of target categories in the image. The comparative experimental results are shown in Table 2.  
In scenarios with low light conditions, a random collection of images was employed to validate the algorithm's performance. Many multi-task segmentation algorithms fail to accurately segment in complex indoor environments under dim lighting, resulting in poor segmentation outcomes for relatively small targets such as chairs and people, often leading to incorrect segmentation. However, the visualization results presented in Figure 7 demonstrate that our method can effectively distinguish individuals even in challenging indoor settings where chairs are obstructed and lighting is insufficient.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & \(mAP\) \\ \hline Mask RCNN & 63.3 \\ \hline PSP\_Net & 68.5 \\ \hline Ours & 70.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results obtained from the experimental scenario dataset.  
Figure 7: Result of object segmentation and feature point matching.