# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 2 Related Works  
Conventional image-based techniques for detecting traffic lights mostly utilized computer vision algorithms [8]. Captured images were first transformed into multiple color spaces. Features were then extracted for detection. In machine learning-based approaches [9], image features such as the histogram of oriented gradients or Harr-like operators were used for support vector machine (SVM) or adaptive boosting (AdaBoost) classification techniques. [PERSON] et al. [10] presented a traffic light detection method that integrates the depth information obtained using a stereo camera. [PERSON] et al. [11] presented a dual-camera system that uses multiple focal length settings to expand the extent for detecting traffic lights. They used a camera with long focal length for faraway traffic light detection and a camera with a wide-angle lens for nearby traffic light detection.  
Several techniques that compute the positions of traffic lights using deep neural networks have been extensively studied. [PERSON] et al. [6; 12] proposed the DeepTLR and HDTLR techniques, which use CNN for traffic light detection and classification. [PERSON] et al. [13] proposed an approach for object detection, recognition, and localization. They introduced a multi-scale method with a sliding window. It can be efficiently performed in a CNN. Recently, several networks for object detection have been utilized for detecting traffic lights. For example, [PERSON] et al. [14] presented an approach that uses the You Only Look Once (YOLO) framework [15] to detect traffic lights. Traffic lights sometimes appeared as small elements in images, and a common solution to this problem was to decrease the stride of a neural network for feature preservation. [PERSON] and [PERSON] [16] used the single-shot multi-box detector method [17] and focused on small traffic light detection. [PERSON] et al. [18] presented a unified traffic light recognition system that can perform state classification (circle, straight, left, right) by using a faster region-based CNN (Faster R-CNN) structure [19].  
Recent methods for traffic light recognition can be divided into two classes. Methods in the first class detect a traffic light, cut the region of the traffic light, and deliver the traffic light information to a classifier for light state recognition [14]. Methods in the second class simultaneously detect a traffic light's position and recognize its light state [16; 18]. When the location of an object is forecasted with a high confidence and a bounding box, an extra branch is utilized for the light state prediction. Other than the recognition of basic circular lights, a traffic light recognition system must typically recognize multiple types of arrow lights used in multiple countries. However, few studies have explored this issue [12; 18]. A two-stage technique is generally used, first classifying light colors and then classifying arrow types.  
One disadvantage of image-based methods for detecting traffic lights is the false positives due to the presence of similar background features. To reduce incorrect detection,a simple solution is to limit the ROI in an image when searching a traffic light. The location of the traffic light can also provide more information that improves the accuracy of detection. In this case, the aim is to ameliorate image-based methods. The traffic light detection method based on maps operates on the basis that traffic lights are placed at steady positions under normal circumstances. Global positioning system (GPS) and light detection and ranging (LiDAR) are commonly used to establish high-definition (HD) maps and annotate traffic light positions on a vehicle's route [20,21]. When a vehicle is moving, map and localization information are utilized to compute the location where a traffic light will appear in a slight region. Furthermore, this information can be used to verify the presence of the next traffic light.