# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 1 Introduction  
Traffic sign detection is the premise for driverless cars to understand traffic information, avoid traffic congestion and accidents, and ensure the safe and orderly driving of vehicles. It is also an essential submodule of driver assistance systems. Currently, CNNs are widely used for traffic sign detection [1; 2; 3; 4], which relies heavily on a large number of accurate bounding box annotations and artificially balanced training classes. When the contextual information of the training and testing sets is distributed unevenly, a serious mistake will occur that fails to generalize. It is a great challenge to guarantee the accuracy and robustness of detection results when samples are limited because of the large variation in object scales, such as vehicle speed, and the inconsistencies in traffic signs due to regional differences. Meta-YOLO draws inspiration from classical CNNs and few-shot techniques. We outline some of the salient works here to set the context. **Traffic Sign Detection by CNNs. ** In recent years, researchers have generally adopted a visual scheme based on deep CNNs (convolutional neural networks) to achieve the task of traffic signs detection. A one-stage detector is based on the regression method, which directly outputs the location and category of the bounding boxes densely in a single-shot, such as YOLOv1-v4 [5; 6; 7; 8], SSD [9] and RetinaNet [10]. A two-stage detector is based ona region proposal, including R-CNN [11], Fast R-CNN [12], Faster R-CNN [13] and Mask R-CNN [14]. In view of the complex background and unbalanced sample distribution, [PERSON] et al. [15], on the basis of a fully study of the relationship between different traffic signs with digital characters, designed an SE block that could automatically learn the importance of each channel from global information. This method simplifies the detection of a wide variety of numerical traffic signs to 10 digital categories, but it is difficult to distinguish similar false targets in complex real traffic scenes. [PERSON] et al. [16] proposed an LW-RefineNet to segment the scene and obtain the information regarding the spatial positional at pixel level, and then the constraint model is constructed to establish the search regions. Experiments show that this method alleviates the mis-detection of small traffic signs. However, it is limited in scenarios where both sides of the road, and other scenarios (such as intersections), have ineffective detection. The above research shows that fully understanding and representing the real features of the extracted traffic signs is an effective solution to distinguish similar objects and filter false associations, which provides a basis for the design of the FDM module in this algorithm. Too small traffic signs are one of the main causes of mis-detection, and improving the multi-scale ability of detection algorithms is a common method to solve the challenge of small target detection. [PERSON] et al. [17] presented an improved Sparse R-CNN and constructed hierarchical residual-like connections within each single radix block, while a cross-channel attention mechanism was added in the RoI division process to fuse shallow feature information. However, due to its single attention mechanism, global correlation in RPN may suffer from spatial scale dislocation, and local correspondence between objects may be ignored. [PERSON] et al. [18] applied an inception and channel attention mechanism to a superclass detector and directly concatenated feature maps of different channels, which overcomes [PERSON] et al. 's [17] complex backbone problems. At the same time, there is a negative impact on robustness because the importance of the different feature channels is not considered. Although the traffic sign detection algorithm based on CNN has achieved remarkable results in real-time performance and accuracy, most methods require a good deal of labeled sample data, and, in fact, our dataset cannot exhaust all traffic scenes.