# A Two-Phase Fashion Apparel Detection Method Based on YOLOv4
## 1 Introduction  
An image is worth a thousand words. The fashion apparel industry is one of the fields that have a large image usage. These clothing images appear in newspapers, magazines, e-commerce platforms, social media, and even advertising boards. There are various object detection applications that extract information from images and apply it to apparel recognition, apparel detection, fashion recommendation, and online search for the fashion apparel industry, which can be used to flourish human life. In recent years, deep learning technology in computer vision has many applications and researches, for example, image classification [1], object detection [2], semantic segmentation [3], and instance segmentation [4]. Among them, object detection technology is a rapidly developing research topic. Object detection methods and other computer vision algorithms are important to the fashion apparel industry. The clothing information marking system based on the object detection method can automatically mark images in social media or online stores and create information tags [5]. The online store can use the created tags for clothing quick search [5] or similar clothing recommendations [6] in the future. Additionally, occupation recognition [7], fashion style recognition [8], and fashion style recommendation [9] are all extended applications in the field of computer vision. Clothing images have a massive distinction with different characteristics of clothing appearance, style, and posture [10]. Different types of clothing might be similar in types of material and color. Thus, the computer finds it difficult to recognize different types of clothing. Due to this difficulty, the task of detecting clothing by using computer vision technology becomes a difficult challenge. With the improvement of GPU's computational capabilities, the field of machine learning and deep learning has had a huge breakthrough. Therefore, deep learning scientists can construct deeper neural networks to solve the unstructured data problem that machine learning algorithms cannot handle. The mainstream object detection algorithms are based on convolution neural networks (CNN) that are divided into one-stage detection and two-stage detection, by using different feature extraction methods. Object detection algorithms that use a two-stage detection method include R-CNN [11], Fast R-CNN [12], and Faster R-CNN [13], which divide the detection task into (1) region proposal and (2) classification. The two-stage detection method has a high detection accuracy, but the problem of high time complexity leads to a longer detection time. Therefore, the two-stage detection method is hard to use in real-time detecting applications. The one-stage detection method integrates region proposal and classification into one step, which reduces the detection time. The mainstream methods of one-stage detection are SSD [14], YOLO [15], YOLOv2 [16], YOLOv3 [17], and YOLOv4 [18]. These methods improve detection speed without losing too much accuracy and are increasingly becoming popular object detection algorithms. Transfer learning is the process of applying knowledge and skills learned in previous tasks to target tasks. According to research by [PERSON] et al. [19], transfer learning is divided into three categories by different domains and tasks (1) Inductive Transfer Learning, (2) Transductive Transfer Learning, and (3) Unsupervised Transfer Learning. In this research, we proposed a fashion apparel detection model based on the You Only Look Once (YOLOv4) algorithm named YOLOv4-TPD (YOLOv4 Two-Phase Detection model for fashion apparel) by using the characteristics of inductive transfer learning. The contributions of this paper are as follows. First, we proposed an object detection model by using two-phase transfer learning for detecting fashion apparel images with complex background. Second, the experimental results proved that implementing two-phase transfer learning and the CLAHE image enhancement method could improve the accuracy of model detection.