Clothing attribute recognition algorithm based on improved YOLOv4-Tiny

Abstract

Aiming at the problem of low accuracy of clothing attribute recognition caused by factors such as scale, occlusion and beyond the boundary, a novel clothing attribute recognition algorithm based on improved YOLOv4-Tiny is proposed in this paper. YOLOv4-Tiny is used as the basic model, firstly, the multi-scale feature extraction module Res2Net is adopted to optimize the backbone network, the receptive field size of each layer of the network is increased, and more abundant fine-grained multi-scale clothing feature information is extracted. Then, the three feature layers of the output of feature extraction network are up-sampled, and the high-level semantic features and shallow features are fused to obtain rich shallow fine-grained feature information. Finally, K-Means clustering algorithm is employed to optimize the anchor box parameters to obtain the anchor box that is more compatible with the clothing object, and to improve the integrating degree between the clothing attribute characteristics and the network. The experimental results demonstrate that the proposed method outperforms the original YOLOv4-tiny network in terms of accuracy, speed, and model parameters, and is more suitable for deployment in resource-limited embedded devices.