# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 3 Approach
### Core Components
#### 3.3.2 Detection and Classification
Additionally, version 3 predicts boxes at several different scales and has also the ability to predict an increasing number of classes at once. Nonetheless, it comes with better performance regarding computation time due to slight changes in the network model as well as improvements in internal calculations. Figure 6 shows the performance of the YOLOv3 suite comparing to other frameworks. YOLO has a much better performance regarding computation time with nearly similar accuracy. YOLOv3 is about three times faster whereas past YOLO versions struggled with small objects. However, version 3 uses multi-scale predictions which produces better results regarding accuracy. The evaluation is based on the COCO dataset ([[http://cocodataset.org/](http://cocodataset.org/)]([http://cocodataset.org/](http://cocodataset.org/))) computed on a NVIDIA Titan X GPU. Figure 5: Prediction outputs from a single image by YOLO. Unfiltered output (**a**) showing all bounding box candidates where an object is suspected. (**b**) only shows predictions with values over a certain confidence factor. Labels representing object classes with the highest score. Source [23; 24]. Based on [24], YOLO has pretrained convolutional layers using the ImageNet 1000-class competition dataset ([[http://image-net.org/](http://image-net.org/)]([http://image-net.org/](http://image-net.org/))). The first 20 layers were pretrained with the underlying Darknet framework. The training was stopped after they reached about the same accuracy as the GoogLeNet models. The rest of the convolutional layers as well as the fully connected layers for detection are added with randomly initialized weights. Due to the base version of YOLO suffers from poor detection results for objects with varying sizes, where [25] proposed a multi-scale training as an improvement. Therefore, every few iterations the input image size changes. The model is now able to predict well across a variety of input dimensions. It can now detect predictions at different resolutions. Additionally, YOLO can also be trained with own datasets. Stated by [23], YOLO is an open source object detector and classifier. The source code can be found on the official Github repository ([[https://github.com/pjreddie/darknet](https://github.com/pjreddie/darknet)]([https://github.com/pjreddie/darknet](https://github.com/pjreddie/darknet))). Due to the fact that the original implementation is only testable and compilable on MacOS or Linux an additional repository ([[https://github.com/[PERSON]/darknet](https://github.com/AlexeyAB/darknet)]([https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet))) provides a Windows version. It is a fork from the original repository and is being maintained continuously. As mentioned before, YOLO comes with pretrained convolutional weights what makes experimenting easier for the users. After installing the base system YOLO is ready to be applied instantly. All necessary files and information can be found on the official project website ([[https://pjreddie.com/darknet/yolo/](https://pjreddie.com/darknet/yolo/)]([https://pjreddie.com/darknet/yolo/](https://pjreddie.com/darknet/yolo/))). Furthermore, there are also versions with additional dependencies available, such as OpenCV and CUDA. According to [23], YOLO is about 500 times faster on GPUs with CUDA support in comparison to CPUs due to its ability to compute several tasks simultaneously. OpenCV is mainly used due to the extended support for image formats as well as viewing the results immediately without the need to store them on the disk first and open it separately. YOLO comes with an own configuration file. It is used for initializing the CNN with basic settings at startup. Additionally, the file includes entries for test and training configurations as well as settings for all the different layers. Table 2 describes the most important settings in more detail. Figure 6: YOLOv3 significantly outperforms other detection methods with comparable accuracy in terms of computation time.