# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 4 Experimental Results and Performance Analysis
### Performance Analysis
#### 4.3.1 Experimental Results on NJRY Dataset  
This paper presents the results of testing the IBCDet algorithm on the NJRY dataset. Four experiments were designed and compared with current mainstream object detection algorithms, including Faster RCNN [42], SSD [43], YOLOv3 [44], YOLOv5, YOLOv7 [45], YOLOV3 and median filtering [27], and CrowdDet [20]. Firstly, Table 2 shows the performance comparison between our proposed method and the aforementioned object detection algorithms on the NJRY dataset. The results in Table 2 show that the IBCDet algorithm has better performance than the other methods. The IBCDet algorithm achieves an AP of 95.30%, \(MR^{-2}\) of 24.44%, and JI of 76.35%, which, respectively, improve the baseline network by 1.3, 19.23, and 2.1 percentage points in terms of AP, \(MR^{-2}\), and JI evaluation metrics as well as achieve the best performance on the NJRY dataset. Additionally, it can be seen from the experimental results that the \(MR^{-2}\) improvement effect is most significant, indicating that our algorithm can effectively reduce the Miss Rate of vehicle detection. To verify the effectiveness of Involution in the backbone network, the third, fourth, and fifth convolutional layers of ResNet-50 were replaced with Involution, and their performance was evaluated, as shown in Table 3. Since the images have a higher resolution and contain more detailed information in the first two layers of ResNet-50 and the features have less spatial variation, Involution was not used to replace the first two layers. From Table 3, it can be seen that the improvement is the most significant when only the fifth layer convolution is replaced: the AP is improved by 1.1%, the \(MR^{-2}\) is reduced by 17.2%, and the JI is improved by 3.3% compared to the CrowdDet algorithm. As Involution dynamically generates different convolution kernels at different positions, it produces different levels of attention, and by using a 7 \(\times\) 7 convolution kernel, it obtains more long-range information. Therefore, the Involution operator can achieve better performance than ordinary convolution, and it can improve the detection accuracy of distant vehicles. To verify the ability of BiFPN to capture contextual semantic information, this study compared the performance of CrowdDet using different feature fusion methods. The experimental results are presented in Table 4. Since BiFPN adds edges to connect contextual information on the basis of FPN and multiplies corresponding weights, it can more effectively acquire contextual semantic information and has better detection performance for vehicles under traffic congestion. As shown in Table 4, the method using BiFPN has better performance than the method using FPN. To test the impact of Involution and BiFPN used in this article on the overall detection results of the model, two modules were verified through ablation experiments. Table 5 shows the experimental results, which indicate that using both modules simultane \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP\%** & **MR\({}^{-2}\)\%** & **JI\%** \\ \hline Faster RCNN & 75.89 & 68.00 & - \\ SSD & 83.24 & 62.00 & - \\ YOLOv3 & 85.13 & 61.00 & - \\ YOLOv3+median filtering & 85.33 & 57.00 & - \\ YOLOv5 & 92.31 & 42.00 & - \\ YOLOv7 & 93.38 & 41.00 & - \\ CrowdDet & 94.04 & 30.26 & 74.76 \\
**IBCDet (Ours)** & **95.30** & **24.44** & **76.35** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results of different methods on the NJRY dataset. The best results are highlighted in bold. ously achieves the best results. This demonstrates that the IBCDet algorithm can accurately detect traffic congested vehicles on an expressway.