# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 3 Methodology
### The Improved Vehicle Detection Network Model Based on CrowdDet Algorithm (IBCDet)  
The cameras installed along expressways typically have a larger viewing angle, which can make it challenging to accurately detect small target vehicles at far distances. In such scenarios, the accuracy of vehicle detection is often low due to the small coverage area, blurred images, and limited feature information. In this paper, we address this issue by introducing the Involution operator [21] into CrowdDet. Involution is a novel type of neural network operator that differs from the traditional convolutional operator in terms of spatial sharing and channel specificity. It exhibits spatial specificity and channel sharing, which allows it to use larger convolution kernels to aggregate contextual information over a wider spatial range, thus overcoming the difficulties of long-range interactions in the model. Additionally, it can adaptively allocate different weights to different spatial positions, thereby enabling richer feature information extraction. As shown in Figure 3, first, the feature vector at a point on the input feature map is subjected to a fully connected operation and a reshape transformation to unfold into a K \(\times\) K \(\times\) G kernel shape, thus obtaining the Involution kernel corresponding to the coordinate point. Then, the Multiply-Add operation is performed with the feature vector in the neighborhood of this coordinate point on the input feature map to obtain the final feature vector. Involution can use larger 7 \(\times\) 7 convolution kernels to capture long-range interactions and dynamically generate kernel parameters based on the input feature map at different positions, thereby facilitating the aggregation of contextual semantic information.  
Figure 3: Schematic illustration of Involution.  
On the other hand, vehicles on expressways have different sizes, and when traffic congestion occurs, vehicles are densely packed and prone to occlusion problems. In order to solve the occlusion vehicle detection problem under congested conditions and improve the feature extraction performance of occluded objects, the baseline detector CrowdDet algorithm uses the FPN proposed by [PERSON] et al. [36], but the simple and crude fusion method of FPN has limitations on accuracy improvement. [PERSON] et al. proposed BiFPN [22], which introduces learnable weights to learn the importance of different input features and repeatedly applies top-down and bottom-up multi-scale feature fusion, as shown in Figure 4.  
To this end, this paper modified the ResNet-50 backbone network of the CrowdDet algorithm, using Involution to replace the ordinary convolutional layer, and proposed InNet50. InNet50 keeps the first four parts of ResNet-50 unchanged, replaces the fifth convolutional layer with the Involution operator, denoted as Inv5, and replaces FPN in the CrowdDet algorithm with BiFPN. The proposed InNet50 network is combined with the BiFPN module. By introducing the Involution network and BiFPN module in the CrowdDet algorithm, the accuracy of vehicle detection in congested scenarios on expressways can be improved. This method is called IBCDet in this paper, and the specific network diagram of IBCDet is shown in Figure 5.  
Figure 4: BiFPN module structure.  
Figure 5: The IBCDet feature extraction and fusion network.