# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 1 Introduction  
With the rapid development of the modern transportation industry, a common scenario in an urban transportation network is that certain sections of the road may experience severe traffic congestion, whereas the traffic flow on nearby sections is relatively smooth. Therefore, by knowing the traffic conditions of each road in real time, the intelligent transportation system can help drivers choose a reasonable driving route, which is also an effective approach to solve urban traffic congestion [1; 2; 3; 4]. The image-processing-based traffic length detection system combines image processing with various traffic information technologies and has the advantages of wide application range, high measurement precision, excellent real-time performance and direct upgrade based on the existing monitoring system. Therefore, it is an important technical component for obtaining modern intelligent traffic information. During the last decade, researchers have conducted extensive studies. In 2014, [PERSON] et al. [5] proposed a visual inspection method named R-CNN (Regions with Convolutional Neural Networks features). This method first divides the inspected image into thousands of different regions, then extractsdeep features of these regions, and finally adopts Softmax for classification. On dataset VOC2012, the mean average precision (mAP) detected by R-CNN could reach 53.3%, which is higher than the precision of previous methods. However, it has the disadvantages of long detection time and difficult engineering application. In 2016, [PERSON] et al. [6] proposed the SSD (Single-Shot Multi-Box Detector) method, which improves the deep convolutional neural network of VGG-16 (Visual Geometry Group Network) [7] and the target detection method of YOLO (You Only Look Once) [8], extracts multi-scale feature maps, and directly outputs the location and class of the detected target. On dataset VOC2007, this method could achieve a mAP of 74.3% and a speed of 59 FPS. In 2018, [PERSON] et al. [9] reported a deep convolutional neural network (CNN) based on multiple tasks and the vehicle detection method for an automatic pilot based on region-of-interest voting. In this method, the multi-task targets of CNN include area overlapping, subclass, regression of detection frame and region of proposals (ROI) regional training, and the vehicle is detected by fully considering the influence of the neighborhood ROI. According to the validation of vehicle datasets KITTI [10] and VOC [11], it presents a much better performance than most similar algorithms. In 2019, [PERSON] et al. used the YOLO-UA model to improve the detection precision of vehicles under complicated weather conditions [12]. In the various studies above, all researchers have ignored a problem, which is that, in actual street lighting, we need to extend the effective detection distance of the video as much as possible to reduce the number of intelligent streetlights, and thus reduce the cost of the entire system. However, in the case of long-distance vision, the accuracy of the current algorithm to achieve fine-grained car classification is not ideal. In order to address this issue, in this article, we primarily make the following three contributions:  
1. In order to realize high-precision and large-scale traffic situation detection, we propose a distributed system based on smart streetlights. 2. Based on the dataset UA-DETRAC, we add the local manually labeled data images in the sight of smart streetlights, establish the classification dataset for SUVs, sedans, taxis, commercial vehicles, small commercial vehicles, vans, buses, trucks, and pickup trucks, and build the dataset UA-DETRAC-LITE-NEW. 3. We optimize the YOLOv3 algorithm in various respects to improve the detection accuracy for distant cars in the sight of smart lights and then combine it with multi-scale training and anchor clustering methods to improve the accuracy of automobile target detection.