## 1 Introduction  
Neural networks, convolutional neural networks (CNNs), and deep CNNs (DCNNs) have led to diverse successes in machine learning. One notable class of successes is the breakthroughs in computer vision, including image classification and object detection. Numerous CNN variants such as VGG16 [1] and ResNet101 [2] have been developed and have achieved distinctive performance in several object detection contests. Scholars have demonstrated real-time vehicle detection with object-proposal-related algorithms [3; 4; 5] based on CNNs. However, few studies have been publishedregarding CNN performance levels under different levels of illumination. We tested models trained with PASCAL VOC 2007 [6], the Faster region-based CNN (R-CNN) [5] model with VGG16 [1], and ResNet101 [2] for feature extraction in low-light environments, and were unsatisfied with the performance levels. Because the defined classes of PASCAL VOC 2007 [6] are different from our predefined classes, we could not directly compare the performance levels under the condition of insufficient lighting. We selected several images containing objects defined in PASCAL VOC 2007 [6], such as pedestrians, bicycles, and cars in urban nighttime environments and extremely dark situations. We defined the images of urban nighttime as everyday city views taken at night with rational ambient lighting. We defined extremely dark conditions as views that lacked illumination from typical sources, such as the headlights or taillights of vehicles. Figure 1 illustrates some urban nighttime images processed by the Faster R-CNN [5] with VGG16 [1] trained using PASCAL VOC 2007 examples [6]. The bounding boxes of purple, red, and blue are for bicycles, cars, and pedestrians, respectively. This approach cannot appropriately detect occluded objects and blurry objects. The blurry outlines of objects may prevent easy identification of object features, especially at night, as mentioned in previous studies [7; 8]. Figure 2 illustrates urban nighttime images processed by the Faster R-CNN [5] with ResNet101 [2] trained using the same portion of the data sets. Figure 1; Figure 2 indicate that ResNet101 [2] provides better performance than VGG16 in detecting partially shown objects. Some objects occupying a few pixels can be observed in Figure 2. However, the abilities to deal with blurry and occluded objects are still weak. Figures 3 and 4 illustrate the results of running extremely dark images through VGG16 and ResNet101 variants of the Faster R-CNN. The Faster R-CNN [5] with VGG16 [1] and ResNet101 [2] trained using PASCAL VOC 2007 [6] are unable to detect the vehicles well, whereas the optimized model trained with our nighttime images can detect them. As depicted in Figure 3, the original VGG16 [1] trained with PASCAL VOC 2007 [6] cannot recognize any objects in the row of motorbikes. As displayed in Figure 4, a system with ResNet101 [2] trained with PASCAL VOC 2007 [6] incorrectly considers an entire row of motorbikes to be a single motorbike. Both original models trained with PASCAL VOC 2007 [6] are incapable of detecting the cars located on the left-hand side under the balcony in the left image. In the present study, we aimed to address these deficiencies. Figure 3: Extremely dark images processed by the Faster R-CNN [5] with VGG16 [1]. **Upper row**: Trained with PASCAL VOC 2007 [6]. **Lower row**: Trained with our nighttime data. Figure 2: Urban nighttime images processed by the Faster R-CNN [5] with ResNet101 [2] trained using PASCAL VOC 2007 [6]. Our predefined classes included bikes (bicycles), buses, cars, motorbikes, pedestrians, vans, and trucks. To maximize the fairness of the comparison, both our collected nighttime data classes and the PASCAL VOC 2007 classes [6] included bikes (bicycles), buses, cars, motorbikes, and pedestrians. The two selected models were the Faster R-CNN [5] with VGG16 [1] and the Faster R-CNN with ResNet101 [2]. These models were trained with PASCAL VOC 2007 [6] and tested using our collected nighttime data for the classes they had in common.