# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 4 Experiments
### Detection Performance in Different Ranges  
To further analyze the performance of the models in different distance ranges, this study employed the Score metric, which encompasses the following components: ACS (Average Center Score): This represents the average score of the object's center point. It is calculated based on the distance or similarity between the estimated center point and the ground truth center point. ACS is used to evaluate the accuracy of the object detection algorithm in estimating the object's center point. \[ACS=\frac{1}{|D|}\sum_{s\in D}\left(1-\text{min}\left(1,\frac{\Delta_{s}^{c}}{ C_{s}}\right)\right) \tag{16}\]  
\(D\) represents the set of true positive samples, \(C_{s}\) is the norm of the ground truth center, and \(\Delta_{s}^{c}\) is the Euclidean distance between the predicted ground center and the ground truth center for sample s. \(|D|\) is the total number of true positive objects. AOS (Average Overlap Score): It represents the average overlap score of the object bounding boxes. It is computed by measuring the overlap between the detected bounding boxes and the ground truth bounding boxes. AOS is used to evaluate the accuracy of object localization. \[AAS=\frac{1}{|D|}\sum_{s\in D}\left(1-\text{min}\left(1,\frac{\Delta_{s}^{A}}{ A_{s}}\right)\right) \tag{17}\] \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{8}{c}{**AP3D/IoU = 0.5**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**GFIops**} \\ \cline{3-3} \cline{5-12}  & & & & & & & & & & & & & \\ \hline KM3D & ResNet34 & _8.97_ & _7.77_ & _3.97_ & _4.94_ & _11.81_ & _11.35_ & _10.39_ & _12.61_ & _26.881_ & _422.276_ \\ \hline Kinematic3D & DenseNet121 & _11.42_ & _10.65_ & _4.02_ & _4.83_ & _11.35_ & _15.08_ & _11.24_ & _14.43_ & _25.913_ & _296.441_ \\ \hline MonoDLE & DLA34 & _12.84_ & _11.21_ & _4.19_ & _5.25_ & _13.23_ & _16.58_ & _11.72_ & _14.75_ & _20.310_ & _264.898_ \\ \hline Ours Base & CSPDarknet & _11.75_ & _10.98_ & _1.86_ & _4.50_ & _14.24_ & _17.46_ & _11.03_ & _13.19_ & _37.626_ & _222.563_ \\ \hline Ours+5 Head & CSPDarknet & _12.25_ & _10.73_ & _1.78_ & _3.68_ & _15.45_ & _18.73_ & _11.66_ & _14.83_ & _42.235_ & _87.625_ \\ \hline Ours + MSSA & CSPDarknet & _13.02_ & _11.33_ & _2.44_ & _4.53_ & _15.69_ & _19.13_ & _11.77_ & _14.81_ & _42.957_ & _88.453_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of the experimental and ablation study. \(\Delta_{s}^{\theta}\) represents the angular difference for sample s, and \(\cos\left(2*\Delta_{s}^{\theta}\right)\) accounts for the fact that during evaluation, no distinction is made between whether the head or tail of an object is facing the camera. ASS (Average Size Score): It represents the average score for object size. It is calculated by measuring the size difference between the detected bounding boxes and the ground truth bounding boxes. \(\Delta_{s}^{A}\) is the absolute area difference, and \(A_{s}\) is the ground truth area. ASS evaluates the accuracy of object size estimation. \[AOS=\frac{1}{\left|D\right|}\sum_{s\in D}\frac{1+\cos\left(2*\Delta_{s}^{\theta }\right)}{2} \tag{18}\]  
AGS (Average Four Ground Points Distance and Similarity): This represents the average geometric distance score (relative value) for object shape. It is calculated by measuring the geometric distance between the detected shape and the ground truth shape of the object. \(\widehat{S_{g}}\) and \(s_{g}\) represent the g-th predicted point and the \(g\)-th ground truth point for sample \(s\), respectively. \(K=4\) denotes the total number of ground truth points. \[AGS=\frac{1}{\left|D\right|}\sum_{s\in D}\left(1-\min\left(1,\frac{1}{K}\sum _{g=0}^{K-1}\frac{\left|s_{g}-\widehat{s_{g}}\right|}{\left|\epsilon\right|} \right)\right) \tag{19}\]  
(\(\text{ACS}+\text{AOS}+\text{ASS}+\text{AGS}\))/4.0: It represents the overall score, which is the average of the four mentioned scores.