## 1 Introduction
By combining the algorithm characteristics of the SPP-net into the R-CNN, the Faster R-CNN algorithm solves problems such as long training and test time and large space occupation, etc. However, the extraction of the proposed box is still based on the selective search method, that is, the time-consuming problem still exists. [14]. By introducing the RPN (region proposal networks) algorithm instead of the selective search (SS) one, the candidate region frame extraction and back end of the Faster R-CNN are integrated into a convolutional neural network model. For that, the Faster R-CNN algorithm can greatly shorten the extraction time of the candidate region [15]. The Faster R-CNN algorithm is considered to be the first truly end-to-end training and prediction, but its speed is far from the requirement of real-time target detection. Based on the candidate box area idea of Faster R-CNN, the concept of Prior Box is proposed in the SSD algorithm. Due to the combination of YOLO's regression thought, the detection speed of the SSD algorithm is the same as YOLO, and the detection accuracy is the same as Faster R-CNN. For that reason, the parameters of the priori frame cannot be obtained through network training automatically and the adjustment process of parameters depends on actual experience, the generalization of the SSD algorithm is not very good. The YOLO series is a regression-based network algorithm that directly uses the full map for training and returns the target frame and target category at different positions. The YOLO algorithm is the first one to choose a method based on the candidate frame area algorithm to train the network. It directly uses the full image for training and returns the target frame and target category at different positions, thereby making it easier to quickly distinguish target objects from the background area but is prone to serious positioning errors. The YOLOv2 algorithm uses a series of methods to optimize the structure of the YOLO network model, which significantly improves its detection speed. For that, its detection accuracy is equal to that of the SSD algorithm. Because the YOLOv2 basic network is relatively simple, it does not improve target detection accuracy. The YOLOv3 algorithm uses the Feature Pyramid Networks (FPN) idea to achieve multi-scale prediction [16] and uses deep residual network (ResNet) ideas to extract image features to achieve a certain balance between detection speed and detection accuracy [17]. However, the size of the smallest feature map (\(13\times 13\)) is much larger than the SSD algorithm (\(1\times 1\)), the positioning accuracy of the object by YOLOv3 is low, and the false detection and miss detection is easy to occur. Our team previously published a YOLO-UA algorithm based on YOLO optimization for traffic flow detection, which mainly realized real-time detections and statistics of vehicle flows by adjusting the network structure and optimizing the loss function [18]. According to the team's achievements and foundation, in this article we investigate the YOLOv3-DL algorithm, a high-performance regression-based algorithm for detecting and collecting statistical information from the traffic flows in real-time. YOLOv3-DL is based on the YOLOv3 algorithm, both Intersection Over Union (IOU) and Distance-IOU (DIOU) are used to enhance its performance. The method of directly optimizing the loss function, which measures the parameters to improve the progress of target positioning, can solve the problems of insufficient positioning accuracy of the YOLOv3 model and low vehicles' statistical accuracy. After optimization, it can be better applied to detect video vehicles in real-time and in real scenes. By optimizing models and algorithms, we can better improve the performance of the detection system of traffic flows.