# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 2 Related Works  
In this section, this paper primarily reviewed the existing literature on traffic congestion detection methods. Vehicle congestion detection acquires various traffic flow parameters through relevant techniques to evaluate the vehicle congestion status. Due to the low cost and easy maintenance of cameras, which can provide high-quality video sequences, and the continuous development of image processing technology, more and more scholars have started to study machine vision-based traffic congestion detection. [PERSON] et al. [9] use Haar-like features to extract the number of vehicles in the image, calculate the vehicle flow rate per unit time, and combine with a threshold to judge the vehicle congestion status. [PERSON] and [PERSON] [10] proposed a vehicle congestion status evaluation system using texture analysis of images. The method uses the duration of vehicle density as the judgment condition for vehicle congestion detection, detects edge information in the image using the Canny edge detection algorithm, analyzes the edge information for vehicle target detection, and calculates the vehicle density on the road surface. However, hand-designed feature-based methods have high time complexity and are not conducive to real-time detection of traffic conditions. In order to overcome the limitations of the above traffic congestion detection methods, studies in the literature [12; 13; 23; 24; 25] use deep learning-based image classification methods for traffic congestion detection. [PERSON] et al. [24] use a convolutional neural network (CNN) to estimate the traffic state, adjust the size of the monitoring image and convert it into a 100 \(\times\) 100 grayscale image as the input for CNN training and testing. The CNN model achieves an average classification accuracy of 89.50% on the monitoring image dataset. [PERSON] et al. [25] labeled images as congested or non-congested states and used YOLO [26], deep convolutional neural networks (DCNNs), and support vector machines (SVMs) for image classification to detect traffic congestion. The results show that the accuracies of YOLO and DCNN are 91.5% and 90.2%, respectively, and the accuracy of SVM is 85.2%. [PERSON] et al. [12] proposed a method of extracting images from a monitoring camera at an ultra-low frame rate, considering that high frame rate videos are difficult to use in real-world situations. They used semantic segmentation to label common items such as vehicles, lanes, and road dividers at the pixel level. Based on the classification of traffic congestion images, they obtained the percentage of road occupancy by vehicles through image transformation to detect traffic congestion at the pixel level. [PERSON] et al. [13] used GoogLeNet to classify images collected from monitoring cameras at intersections of urban roads in London and output the congestion level of the roads. [PERSON] et al. [23] proposed an image-based traffic congestion estimation framework that integrates traffic parameters into the convolutional neural network layer to directly detect traffic congestion. Although classification-based methods perform well in detecting traffic congestion, this approach only classifies images into two states: congested and uncongested, ignoring the various complex scenes that occur during traffic congestion. In addition, the literature [11; 27; 28] proposed real-time discrimination methods for urban road traffic congestion. [PERSON] et al. [11] used a road congestion index and network congestion index to, respectively, measure the degree of congestion on urban roads and road networks. [PERSON] et al. [27] proposed the mIOU method, which detects traffic congestion by calculating the ratio of the overlap area between two images taken at a certain time interval and the union of multiple bounding boxes. Since the instantaneous mIOU proposed in the literature [27] performs poorly when the time interval is short, [PERSON] et al. [28] proposed a new weighted mIOU method.