# Swin Transformer-Based Object Detection Model Using Explainable Meta-Learning Mining
###### Abstract  
In order to minimize damage in the event of a fire, the ignition point must be detected and dealt with before the fire spreads. However, the method of detecting fire by heat or fire is more damaging because it can be detected after the fire has spread. Therefore, this study proposes a Swin Transformer-based object detection model using explainable meta-learning mining. The proposed method merges the Swin Transformer and YOLOv3 model and applies meta-learning so as to build an explainable object detection model. In order for efficient learning with small data in the course of learning, it applies Few-Shot Learning. To find the causes of the object detection results, Grad-CAM as an explainable visualization method is used. It detects small objects of smoke in the fire image data and classifies them according to the color of the smoke generated when a fire breaks out. Accordingly, it is possible to predict and classify the risk of fire occurrence to minimize damage caused by fire. In this study, with the use of Mean Average Precision (mAP), performance evaluation is carried out in two ways. First, the performance of the proposed object detection model is evaluated. Secondly, the performance of the proposed method is compared with a conventional object detection method's performance. In addition, the accuracy comparison using the confusion matrix and the suitability of real-time object detection using FPS are judged. Given the results of the evaluation, the proposed method supports accurate and real-time monitoring and analysis. Swin Transformer; object detection; meta-learning; explainable AI; data mining; anomaly detection +
Footnote †: journal: Medical Image Evaluation  
based on CNN, the locality of the convolution operation has the capacity limit to extract global context information [6]. Therefore, it is necessary to apply a method of solving the capacity limit problem and improving prediction accuracy. The Vision Transformer (ViT) model processes and classifies patches of an image. The method makes it possible to focus on the dependence of image feature patches globally. Additionally, it is possible to maintain the space information for objection detection through multi-head self-attention learning [7]. Therefore, since the method is capable of extracting more context information and learning feature expressions that can be judged, accuracy can be improved. The Vision Transformer model, however, requires numerous data points for learning. In reality, open data for ViT model learning are not enough. For this reason, it is necessary to apply a method of letting a model learn enough with a small quantity of data. Meta-learning makes it possible to do so. There are a variety of meta-learning methods, including Few-Shot Learning, hyper-parameter optimization, and meta-expression. Generally, a deep learning model is categorized as a black box model [8]. In a black box model, it is possible to find inputs and outputs but is difficult to determine the grounds, causes, and reasons for internal work. Since there is a lack of validity in the process of drawing the result to be determined by the model, the reliability of the result is low. To solve the problem, an explainable artificial intelligence (AI) model was developed. It is a white box model with the addition of explainability to determine not only inputs and outputs but a final result [9]. This study proposes a Swin Transformer-based object detection model using explainable meta-learning mining. The proposed method merges the Swin Transformer as a type of vision transformer and the YOLOv3 model, applying meta-learning and devising an explainable object detection model. Based on the feature pyramid network of YOLOv3, it considers objects of an image in multiple scales. To improve the detection accuracy of small objects, the method makes it possible to consider various patch sizes through the Swin Transformer model. Therefore, it supports more detailed learning.