## 3 Proposed Method
### Training Strategies  
#### Data Set Collection Scheme and Labeling Conventions  
We collected 9003 nighttime images, including images with urban nighttime and extremely dark views. These images were labeled with reference to the labeling style of the PASCAL VOC Challenge [12]. Our predefined classes included bikes, buses, cars, motorbikes, pedestrians, vans, and trucks. The real traffic data sets were collected from Taipei City, Taiwan, during nighttime. Some images were taken under slightly bright conditions or relatively dark conditions. Some images were collected with forward- and backward-driving recorders so that we could obtain both the front and back images of vehicles. For the right and left sides of vehicles, we collected data through static photo shooting. All the images were scaled from their original sizes to \(500\times 375\) pixels. Figure 6 illustrates two labeling samples. The left image is a sample of nighttime data collected under somewhat bright conditions in an urban area, whereas the right image was collected under insufficient ambient illumination in a remote area. These two samples reveal our labeling convention in general cases, where pedestrians, motorbikes, cars, bikes, and buses are labeled using purple, red, sky-blue, deep-blue, and green boxes, respectively. Nevertheless, we still use certain special definitions for specific cases. Some objects, such as a row of motorbikes parked along the roadside, are occluded. They should be labeled if their major features are distinguishable. For example, if their major parts and outlines are recognizable, then they should be labeled. We mostly did not label objects when they were blocked or covered by other objects in front of them (i.e., when the crucial features were invisible). Although many occluded objects are present in Figure 7; Figure 8, their major features and outlines are visible. They should be labeled because the instances can be distinguished. Figure 9 depicts an example of an object blocked by another object in the foreground. Although we knew that two people were riding a motorbike, we still labeled the object as one person on a motorbike because the driver's major features and outlines were blocked by the passenger. Such labeling under relatively bright images is crucial during the labeling of extremely dark images, because even the human eye finds it extremely difficult to distinguish the number of objects. Our labeling conventions focus on the visibility of major features and outlines instead of inferring whether an object is present. Figure 6: Two labeling samples. **Left**: Brighter nighttime image. **Right**: Darker nighttime image. Figure 7: Occluded motorbikes with clearly visible outlines and noteworthy features. **Left**: Part cropped from unlabeled data. **Right**: Part cropped from labeled data. Some objects occupy only a few pixels. We usually call them small objects. However, we must clearly define the term \"small object\" in our labeling conventions, especially in nighttime object detection. The major difficulty of nighttime detection is that the environments are lacking ambient illumination. If we include the challenges of blurry objects and occluded objects, the difficulty of nighttime detection increases. We do not call them \"small objects\" because, in the case of object detection, the word \"small\" should refer to the pixels occupied by the object. For example, an object may occupy \(100\times 100\) pixels in an image with a size of \(1920\times 1080\) pixels. We may call this object a small object. Nevertheless, it occupies a sufficient number of pixels for several convolutional operations. Therefore, we must consider the actual pixels occupied by the objects but not the sizes relative to other objects or the image sizes. The detection performance is also related to whether the features are diminished after a certain number of traditional convolutional operations, such as VGG16 [1]. A \"small\" object can still be classified by a model if that object occupies a sufficient number of pixels.