# Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique
## 2 Related Works  
Currently, deep learning is a popular academic and technological topic which is applied in various AI techniques, including those associated with self-driving cars and transportation. Traffic flow monitoring using video has been applied to different traffic scenes, such as city roads, highways, and intersections [1; 2]. Deep learning research applied to solve traffic flow predictions has also developed in recent years. In the 21 st century, researchers proposed a deep learning-based traffic flow prediction method to identify various cars running on the highway. For example, the software tools developed by [PERSON] et al. used the stack autoencoder (SAE) approach to represent traffic flow features maps for predictions. The greedy layer-wise unsupervised learning algorithm was applied to pretrain the network before the model's parameters were updated using a fine-tuning process to improve the prediction [3]. There are various other deep learning models that were developed to predict traffic flow. For example, [PERSON] et al. proposed a deep learning model to predict short-term traffic flow [4]. [PERSON] developed a model which is a combination of a linear model with L1 regularization and some tanh layers [5]. [PERSON] et al. designed a deep learning predictor that takes an input vector and output via different layers [6]. They developed a linear model using previous measurements to provide a forecast. The techniques for object detection and object tracking are important for vehicle counting. For object detection, there are object detectors such as YOLO, Faster R-CNN, SSD, etc. YOLO is a state-of-the-art, real-time object detection system [7; 8]. This object detection method proposed by [PERSON] et al. used regression methods to calculate the bounding box and the related classification probability of the detected objects. A single neural network is used to predict the bounding box and classification probability directly from the complete image in one evaluation. Since the entire detection is a single network, the end-to-end detection performance can be directly optimized [7]. YOLO can solve the special regression problem of object detection frames, and can identify the object and predict the bounding box simultaneously. Therefore, YOLO is one of the most widely used object detection tools, and is especially powerful in high real-time detection tasks. Moreover, newer versions have been developed to improve detection performance. YOLO v2 has a higher accuracy, has a higher efficiency (by adding a batch normalization layer into the convolutional neural networks), and has a high-resolution classifier. YOLO v3 uses multi-label classification, in which an object may belong to multiple categories at the same time. YOLO v3 replaces the softmax function with an independent logistic function to calculate the probability that the input belongs to a specific label. In addition, instead of using the mean square error to calculate the classification loss, YOLO v3 uses the two-class cross-entropy loss for each category. This approach also reduces the computational complexity brought about by the softmax function. YOLO v3 uses DarkNet-53 as the main backbone of the convolutional neural networks to extract features from the input image, which has a better efficiency and detection performance than other backbones [9]. The architecture of DarkNet-53 is shown in Table 1.  
YOLO v4 was released in April 2020 and has a higher accuracy and efficiency for object detection, with reduced hardware requirements [10]. YOLO v4 established an efficient and powerful object detection model, verified the influence of the state-of-the-art Bag-of-Freebies and Bag-of-Specials object detection methods in the training process, and improved the techniques of optimizing the network during training and testing. YOLO v4 uses CSPDarknet53, a combination of Darknet53 and the Cross Stage Partial Network (CSPNet), [11] as the main backbone of convolutional neural networks.