## 2 Related Work
The convolutional part of ResNet101 [2] was used as the feature extractor in our nighttime vehicle detection system. A study [10] investigated the detailed behavior of the Faster R-CNN [5] for vehicle detection. The study indicated that the detection accuracy of the Faster R-CNN [5] increases with the size of the training images. This phenomenon occurs mainly because larger images display the main features and outlines of vehicles more clearly. In addition, the detection performance for testing images varies when an inconsistency exists between training and testing image sizes. This implies that the localization performance improves when the number of bounding boxes retained throughout the detection process is increased; however, increasing the number of bounding boxes may affect the precision. Consequently, reasonable settings for image input sizes, labeling methods, and training processes are crucial to achieve a suitable overall model performance. Apart from the input size, labeling method, and process of training, the transfer learning technique is the most common method for training a model with relatively limited training data. In our study, we collected and labeled images by ourselves. This project did not require a large-scale collection and labeling campaign. Therefore, we used transfer learning, which initializes the model parameters with those trained in the large-scale image recognition contest. The most common transfer learning parameter is ILSVRC-2012-CLS [11], which contains millions of training data. When using their trained parameters for initialization, we must fix a certain number of neural network layers and free the rest so that the model can adapt to our collected and labeled nighttime data. For the selection of the input size and labeling method, PASCAL VOC Challenge [12] is one of the most popular methods for treating the image data. In the training and testing phases, most of the horizontal images had a size of \(500\times 375\) pixels. Images of this size can clearly display the crucial features and outlines of vehicles without excessively increasing the processing time. The labeling convention from this study also indicates how we should label the data for detection purposes. We followed their approach and altered labels according to the behavior of nighttime images. The traditional methods of nighttime vehicle detection applications use sensors [8] or algorithms that heavily rely on vehicles' headlights and taillights [7; 13]. The authors of [7] stated that the methods based only on images depend on the headlights and taillights of the target vehicles. The algorithm is mostly suitable for the urban or city nighttime environments. Thus, a certain degree of ambient lighting, such as the street lights, is required. The method in [13] first applied traditional image preprocessing on the input frames and performed bright component information process. After that, they distinguished different cars in different lane and perform taillight clustering. Consequently, they compared current frames with previous frames in order to perform the tracking operation. Their proposed method is capable of dealing with urban and general nighttime vehicle detection; however, it is impracticable under extremely dark conditions. The technique proposed in [14] performed a global rule-based vehicle detection which contained three major steps: Bright spot segmentation, candidate taillight extraction, and candidate taillight pairing. They used an improved Otsu thresholding method, which can solve the problem of small portion of pixels of those taillights. After that, they performed the connected-component extraction in order to locate the bright spots with the optimal threshold. Then, the headlights could be paired to locate possible vehicles. However, headlight pairing may encounter a problem which it may be frustrated by two parallel motorbikes. The traditional method does not work well in extremely dark conditions with no headlights and taillights. There are more approaches based on machine learning techniques.