## 1 Introduction  
The World Health Organization (WHO) announced the COVID-19 outbreak, which began in the city of Wuhan on 30 January 2020, as a Public Health Emergency of International Concern on 11 March 2020 and a pandemic on 11 March 2020 [1]. To minimize the virus spread, individuals are turning to an online-based system, putting aside social distances to avoid unwanted outings, and closing all schools and offices. As a result, customers are shifting their shopping habits to E-commerce platforms to purchase items; consumers are spending more time at home, limiting their ability to shop physically [2]. The fashion industry generates a staggering amount of revenue, often in the billions of dollars, which is directly related to social, cultural, and economic issues in the real world. Clothing is an inescapable requirement for improving personal look in people's lives. The rapid development of online platforms has led to the proliferation of a great deal of data on social media (the Internet), including clothing data. This, of course, affects how vendors meet customer needs on E-commerce platforms. They eventually saw the value of E-commerce. Stressful occurrences, such as pandemics, also result in long-term behavioral changes and alterations to consuming habits to adjust to new life circumstances. We discovered, for example, that online customers participate in both problem-solving and emotional coping activities [3]. Prior to the pandemic, one of the keys to the success of E-commerce platforms was customer review ratings, but not after the outbreak [4]. As a result, experts are conducting extensive research to improve the quality of E-commerce and retain existing customers while attracting new ones. Customer satisfaction is a critical aspect of the success of an E-commerce platform [5]. Customer happiness is directly related to identifying items that satisfy the unique demands of each consumer. The more relevant information we can display about a customer's requirement during the search and suggestion process, the higher the customer satisfaction. The preceding paper [6] examined the system's fault in automatic category recommendations on the Shopee Thailand E-commerce portal. This research can assist buyers in avoiding seller fraud and determining which categories are frequently mis-selected by vendors, particularly in the fashion area. When sellers pick a category for their items, 75.1 percent of products are assigned to the \"other\" category, despite distinct fashion categories. Due to incorrect categorization, undesirable categories are presented when the consumer searches for items by category. Merchants' lack of comprehension of product category selection might lead to a loss of customer confidence in the E-commerce platform. Object detection is one of the most successful approaches to solving this problem. Image processing and computer vision heavily depend on object detection ability [7]. Various Computer Vision techniques are frequently utilized in numerous applications, including clothing detection [7, 8], clothes collocation [9, 10, 11], clothing attribution and category recognition [12], and fashion image retrieval [13, 14, 15, 16, 17, 18]. Object detection techniques are used in practically every aspect of life; the most notable are surveillance [19, 20, 21, 22], autonomous driving [23, 24, 25], pedestrian detection [26, 27, 28], and the fashion industry [29, 30, 31, 32]. Object detection aims to identify distinct item categories and precisely locate category-specific objects using bounding boxes. These applications process rich mid-level images using deep learning technology along with effective feature learning [33, 34, 35]. During the last two decades, it has been widely accepted that developments around object detection occurred in two distinct historical periods. 2014 marked the transition from traditional object detection to object detection based on deep learning [36].