## 4 Results and Discussion
### Dataset and Evaluation Metrics  
The Cityscapes dataset records the urban street scenes data of 50 European cities in several seasons such as spring, summer and autumn. It is composed of 5000 finely annotated images with both semantic and instance information and 20,000 coarsely annotated images with only semantic information that are all at a resolution of 2048 \(\times\) 1024 pixels. There are 19 object classes in the Cityscapes dataset, including 11 \"stuff\" classes and 8 instance-specific \"thing\" classes. Moreover, 5000 finely annotated images are divided into 2975 for the training set, 500 for the validation set and 1500 for the test set. The task of instance segmentation is to complete the detection and segmentation of 8 instance-specific classes. We have counted the respective object number of 8 instance classes in the Cityscapes dataset, as shown in Table 2.  
In addition, as shown in Table 3, we make statistics on the scale of 8 instance classes according to the size of each class. In Table 3, the scale of each instance object is obtained by multiplying the width and height of the object and then taking the square. The mean, range and standard deviation are found by statistical analysis of scale values. The drastic change of object size and scale as well as the dynamic scene of object occlusion and aggregation increase its complexity, which makes it a challenging dataset for instance segmentation methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Contents** \\ \hline Processor & Intel i9-10940x \\ Graphics card & NVIDIA Titan V \\ CPU memory & 128 GB \\ Graphics memory & 24 GB \\ Deep learning framework & Pytorch [37] \\ Deep learning toolbox & MMDetection [38] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware and software environment of experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Car** & **Rider** & **Bicycle** & **Person** & **Bus** & **Train** & **Motorcycle** & **Truck** \\ \hline
**Size (k)** & 26.9 & 1.8 & 3.7 & 19.9 & 0.4 & 0.2 & 0.7 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of each instance class in the Cityscapes dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean (pixel)** & **Range (pixel)** & **Standard Deviation (pixel)** \\ \hline
**car** & 101.4 & 1.7–850.7 & 104.5 \\
**rider** & 76.6 & 6.5–474.6 & 64.2 \\
**bicycle** & 73.8 & 3.9–538.1 & 61.5 \\
**person** & 63.1 & 2.0–669.8 & 61.5 \\
**bus** & 160.2 & 6.9–816.6 & 146.6 \\
**train** & 255.0 & 14.5–1045.8 & 230.3 \\
**motorcycle** & 93.0 & 4.5–462.0 & 76.6 \\
**truck** & 143.9 & 9.8–908.3 & 145.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The scale statistics results of each instance class in the Cityscapes dataset.  
Besides, we employ average precision (AP) as the evaluation metric to assess the performance of the instance segmentation method. Explicitly, we follow the Cityscapes dataset instance segmentation evaluation criteria and adopt 10 overlaps in the range of 0.5 to 0.95 in steps of 0.05 to avoid bias for some specific values. As minor metric, we add AP\({}_{50}\) for an overlap value of 50% as well as frames per second (FPS) to assess the performance of the network.