## 4 Evaluation Metrics  
A commonly used metric for measuring the performance of object detection is Average Precision [103]. Precision, average precision and mean average precision are widely utilized metrics for evaluating performance of 2D object detection. For 3D object detection, different benchmark competitions use average precision (AP) as well. AP is used for performance comparison in [37; 61; 128; 129]. Average Orientation Estimation is also used as 3D object detection evaluation metric which shows the angle of the object [44; 129]. A slightly different version of AP is Mean Average Precision, used by [32; 42; 44; 129].  
[PERSON] et al. used Average Precision at different IoU (Intersection over Union) values, i.e., 0.5 or 0.7, for evaluation [56]. IoU is used to measure the amount of matched area between ground truth and the detected object. If there is a perfect match, i.e., the object is perfectly predicted, then the IoU value will be 1.  
If TP indicates True Positive (correctly detected object), FP means False Positive (mistakenly detected as object), TN means True Negative (correct mentioning of absence of object) and FN means False Negative (could not predict the presence of object), then, from [130; 131], the simplified equations of precision and recall are as follows:  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{1}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{2}\]  
The parameters of precision and recall are explained in Figure 4. The figure illustrates the meaning of TP, FP, TN and FN.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Domain**} & \multirow{2}{*}{**Ref.**} & \multicolumn{4}{c}{**Sensor Data Type**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{3-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \hline Indoor objects & & & & & & & & & & \\ (Robotic Vision) & [61] & & & & & & & & & \\ \hline \multirow{4}{*}{Precision Agriculture} & [123] & & & & & & & & Self-made \\ \cline{2-10}  & & & & & & & & & Self-made \\ \cline{2-10}  & & & & & & & & & Self-made \\ \cline{2-10}  & & & & & & & & Narrow beam & & \\ \cline{2-10}  & & & & & & & SONAR & & & \\ \hline Human & & & & & & & & & & \\ Pose/ & & & & & & & & & \\ Activity & & & & & & & & & \\ Detection & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multimodal modality techniques of 3D object detection methods from 2017 to 2022.  
In Figure 4a, when the object (in this case the bicycle) is present in the image and it is detected, this scenario is called true positive. In Figure 4b, there was no bicycle in the image, but it was falsely detected. This scenario is called false positive. In Figure 4c, there was no bicycle and there was no detection of bicycles either. It is classed as a true negative. In Figure 4d, there was a bicycle, but it was not detected by the model. This is called false negative.  
If the precision and recall curve is _p(r)_, then the formula to compute average precision is:  
\[\text{AP}=\int_{0}^{1}p(r) \tag{3}\]  
Mean Average Precision (mAP) is computed by computing the average value of all the AP corresponding to an entire dataset. If a dataset has 10 categories, then after finding the AP for each category the mean is computed for these 20 categories to obtain mAP.