# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 2 Related Works  
### Object Detection  
The first object detector was called the Viola Jones object detector [9], proposed by [PERSON] and [PERSON], which was technically classified as an object detector and mainly used in face detection. It provided a real-time solution and was used in many types of computer vision software. With the combination of deep learning and computer vision, the field of object detection is developing rapidly. The first object detector model based on deep learning was the OverFeat network [10] which used convolutional neural networks (CNNs) along with a sliding window approach. It classifies the various parts of the image one by one and then combines the results to produce the final prediction set. The application of CNNs to solve detection problems led to the trend seen in recent years.  
Object detector models have undergone various changes since 2012. The RCNN [11] made a landmark contribution to target detection. It uses a selective search to find around 2000 regions where  
Figure 1: Some examples of poor imaging conditions, the top images are the RGB images, and the bottom images are the images after edge detect. There is a lot of interference information in the edge information under the influence of light and reflection, in these cases the object detector may miss an object or detect a wrong object.  
objects are most likely to be present in them. These regions are cropped from the input image and resized to 7 by 7 pixels, then fed into the object detector model. The RCNN was the first two-step approach detector. However, due to its time and space inefficiency, a better model was needed. Hence, the fast RCNN [12] was proposed soon after, which further improved upon the RCNN. The fast RCNN reduces the overhead of running a region proposal by cropping the regions from a feature map instead of an input image. Additionally, it introduces a simpler single step training pipeline and a new loss function, and this loss function is easier to train and does not suffer from the gradient explosion problem.  
Based on the RCNN and fast RCNN, [PERSON] et al. proposed the faster RCNN in [13], which is a detector that learns end to end. The faster RCNN introduced the region proposal network (RPN), which used feature maps to generate object proposals instead of the selective search. The RPN has the capability of predicting regions of multiple scales and aspect ratios across the image by using a novel concept of anchors, in which the scale invariance is an important property of computer vision systems. One of the basic tasks of object detection is to recognize multi-scale objects. A feature pyramid is the most commonly used method but it is computationally and memory intensive. The feature pyramid network [14] (FPN) provides a top-down architecture with horizontal connectivity to construct high-level semantic feature maps at various scales, and the new state-of-the-art results were obtained in object detection, segmentation and classification by integrating FPNs into the pre-existing models.  
The single shot multibox detector (SSD) [15; 16] was published in 2015, the major difference between the SSD and previous architectures is that it was the first one to propose training on a feature pyramid, and high accuracy and recognition speed can be obtained by the SSD. You only look once (YOLO) [17], proposed by [PERSON] et al. in 2016, has similar architecture to the SSD. As a new target detection method, YOLO is characterized by rapid detection and high accuracy. The authors consider the target detection task as a regression problem of the target region and category prediction, so they used a single neural network to directly predict the object boundary and category probability, and to realize the end-to-end object detection.