## 1 Introduction
Because the two data sets had different distributions and objectives, we could not directly justify the models' performance on our nighttime data. However, from Table 1, we can still obtain intuitions regarding the unsatisfactory performance levels of the models on nighttime images in the context of the effects shown in Figures 1-4. After testing the two models with two metric calculation methods, the 11-point interpolation (TRECVID) used in PASCAL VOC 2007 [6] and the method used in PASCAL VOC 2010 [9] provided a mean average precision of approximately 0.2. The model with ResNet101 [2] marginally outperformed the one with VGG16 [1]. The models are expected to perform better if the training data are collected during nighttime with enhanced labeling convention and fine adjustment aiming at feature extraction and the recognition of blurry objects. \begin{table}
\begin{tabular}{c c c|c} \hline \hline \multirow{2}{*}{**Classes**} & \multicolumn{3}{c}{**Average Precision (AP)**} \\ \cline{2-4}  & **VGG16** & **ResNet101** \\  & **VOC2007** & **VOC2010** & **VOC2007** & **VOC2010** \\ \hline Bicycle (Bike) & 0.1643 & 0.1330 & 0.1915 & 0.1570 \\ Bus & 0.1924 & 0.1642 & 0.2716 & 0.2442 \\ Car & 0.3787 & 0.3670 & 0.4251 & 0.4150 \\ Motorbike & 0.1343 & 0.1005 & 0.1477 & 0.1246 \\ Pedestrian & 0.1618 & 0.1131 & 0.1698 & 0.1244 \\
**Mean** & 0.2063 & 0.1756 & 0.2412 & 0.2130 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of common classes inferred by the models trained with PASCAL VOC 2007 [6] for our nighttime data. Figure 4: Extremely dark images processed by the Faster R-CNN [5] with ResNet101 [2]. **Upper row**: Trained with PASCAL VOC 2007 [6]. **Lower row**: Trained with our nighttime data. To overcome the aforementioned difficulties in nighttime vehicle detection, this study proposes solutions for data collection, the labeling convention of nighttime image data, and a specifically optimized system based on the Faster R-CNN model [5]. As indicated by the experimental results, we obtained a mean average precision (mAP) value of approximately 0.8497. Regarding the solutions of data collection, the original models and the data sets used to train those models were incapable of achieving decent performance levels because of the characteristics of the data sets, as well as the improvements and optimizations of models, specifically for urban nighttime conditions. For extremely dark conditions, the performance levels obtained by the current models trained with the current widely used data sets were unsatisfactory. We labeled the collected data with reference to the horizontal images of size \(500\times 375\) pixels in PASCAL VOC 2007 [6]. In addition, to deal with the special exigencies of nighttime situations, several arrangements were made regarding occluded objects, blurry objects, and small objects. We conducted as much labeling as possible for occluded objects (such as rows of motorbikes). This labeling was intended to provide our model with the capability of detecting occluded objects. We labeled blurry objects as much as possible; however, we discarded a few severely unclear or invisible objects. We treated the labeling of blurry objects as a mission-critical task because our system may be deployed in vehicles, in which case, blurry or shaky images are inevitable. We provided small objects with clear definitions that related to the pixels but not the relative size. Because of the fixed behavior of convolutional operations, objects consisting of a small number of pixels may suffer from loss of features after processing in several convolutional layers. We tested and explained how feature extractors with shortcut linkages were useful for these small objects, because residual networks maintained features [2]. These data were collected from urban nighttime environments and extremely dark conditions, which allowed our system to obtain satisfactory performance in both environments, as indicated by the mAP records and subjective visual comparisons.