Research in clothing image recognition provides an efficient image processing solution for e-commerce and fashion industries. In recent years, research in the field of clothing image recognition has focused on clothing classification, clothing segmentation and attributes recognition.
Many research efforts have been devoted to improving the accuracy of clothing classification. Shajini et al. [12] proposed a T-S pair model based on knowledge sharing and semi-supervised multitask learning for clothing classification and attributes prediction, and validated the feasibility of the approach on the DeepFashion dataset. Zhou et al. [13] proposed a method to improve the classification accuracy of clothing images via DenseNet201 network, transfer learning and optimized RVFL. Despite the high classification accuracy of the DenseNet201 neural network, its large number of parameters makes it difficult to ignore the latency problem in practical applications.
There are also many research works focusing on segmentation of clothing to enable fine image editing of clothing. Inacio et al. [14] proposed a framework called EPYNET for extracting clothing features, which is based on SSDs and FPNs, with the EfficientNet model as the backbone to improve the accuracy of segmentation. Zhang et al. [15] proposed a new framework called ClothingOut, a new framework that utilizes GAN to solve the clothing transformation problem from images containing human bodies to flat clothing images.
Further, research on clothing images has evolved to recognize local attributes of clothing. Chun et al. [16] proposed SAC network for clothing attributes recognition by combining self-attention mechanism with CNN. And they self-constructed a new clothing dataset for predicting fashion styles with 8 attributes. Gu et al. [17] proposed a novel clothing attributes recognition algorithm based on improved YOLOv4-Tiny, which improves the accuracy of clothing attributes recognition. Xiang et al. [18] designed an R-CNN clothing attributes recognition algorithm that utilizes L-Softmax loss. This algorithm performs well in identifying clothing attributes such as shirt collar shape. Li et al. [19] automatically identified and segmented the sleeves of shirts and optimized the LSSVM parameters by using PSO algorithm, which achieved better results in the case of small samples. Zhu et al. [20] proposed sRA-Net to accurately obtain attributes representations by utilizing multiple latent relationships in clothing images to improve the performance of fashion attributes recognition.
In this study, fine-grained attributes recognition of clothing is considered as an object detection task. Different from the previous methods, a dataset of dresses containing 20 attributes is constructed in this paper. By adopting a one-stage object detection algorithm without the need for complex localization and classification steps, and optimizing to reduce computational resource consumption, better detection performance and higher detection efficiency have been achieved.