## 2 Related Work  
Object detection is a popular research area in the field of deep learning. Scholars have presented various new object detection structures. Among the structures related to regions, the R-CNN family is popular [3; 4; 5]. R-CNN [3] uses selective search, which is a clustering algorithm for region proposal, to obtain candidates that may be predefined objects. After using selective search, R-CNN [3] uses CNNs to perform feature extraction on the selected object candidates. After the feature maps have been extracted from the previous CNNs, they are sent to the support vector machine (SVM) for classification. Although the initial bounding boxes are inaccurate, R-CNN performs regression for refining the bounding boxes so that the final bounding boxes are relatively accurate. The Fast R-CNN [4] was proposed to improve the computational efficiency of R-CNN [3]. The Fast R-CNN introduced region of interest (ROI) pooling, which provides the feature maps and the matrices representing all ROIs. ROI pooling involves the projection of ROIs to feature maps according to the imputed image. Then, the projected area is divided into sections of equal size. After ROI pooling has been conducted, the resultant maps are sent to fully connected layers for classification and bounding box regression. The Faster R-CNN [5] is an end-to-end architecture. It dramatically improves the computational efficiency by completing the entire computational process with graphics processing unit (GPU) resources. The input images are sent to CNNs for feature extraction. Then, region proposal networks (RPNs), which are convolutional sliding windows with anchor boxes, are used to select the object candidates. The resultant maps selected from the RPNs are subjected to ROI pooling with the corresponding feature maps. After ROI pooling, the results are sent to fully connected layers for classification and bounding box regression. The CNNs used for feature selection are shared with RPNs to reduce the computational time. CNNs are state-of-the-art structures for image recognition. VGG16 [1] is an excellent baseline CNN that performs well in several image recognition contests. Many object detection systems use VGG16 [1] for feature extraction. VGG16 can use regular \(3\times 3\) kernels to achieve satisfactory performance levels in image recognition. The receptive field of several \(3\times 3\) kernels can replace the receptive field of larger kernels. VGG16 [1] uses two sets of two \(3\times 3\) kernels and the following max pooling layer, as well as two sets of three \(3\times 3\) kernels and the following max pooling layer for convolutional feature extraction subsystems. In the object detection model, these convolutional subsystems are used as the feature extractor for later detection and the classification architecture. Some systems use VGG16 [1]. Residual network families (ResNet) [2] are new baseline CNN models for image recognition. They use residual blocks, which are the shortcut linkages between layers, to reduce the effect of the degradation problem in deep neural networks. The residual blocks introduce a batch of identity projection that can allow the raw information to be processed through the deep layers. However, several \(1\times 1\) components exist that can increase the nonlinearity and perform dimensionality reduction and increment throughout the entire feature extraction process. ResNet101 [2] performed well in various image recognition contests, which proved that shortcut linkages are essential for increasing the depth in DCNNs. In our nighttime vehicle detection system, we must address some objects that occupy only a few pixels, as well as some blurry objects. Such challenges are inevitable in crucial situations, such as embedded systems used in moving cars. We believe that shortcut linkages can be used for dealing with small and gloomy objects after many convolutional operations, even though these convolutional operations may cause feature loss.