基于ClothResNet模型的人体衣物颜色识别

Human clothing color recognition based on ClothResNet model

针对人体衣物属性识别存在衣物分割困难、颜色易扭曲、识别效率和准确率低的问题,提出一种基于ClothResNet模型的高效人体衣物颜色识别方法,模型以ResNet18网络为基础,设计优化的金字塔池化模块以捕获图像中的多层语义信息,融合坐标注意力机制以关注人体衣物轮廓信息,融入空洞卷积以提升网络效率,从而利用多重信息提高人体衣物识别精确率。针对人体衣物属性数据集不足的问题,利用公开数据集活动模板(回归ATR)原始彩色图像构建衣物属性数据集pcaparsing。性能对比实验表明,ClothResNet模型在衣物颜色属性识别中的平均精确率达到9449%,结果均优于同类方法。该识别方法在中小规模的人体衣物颜色识别系统具有广泛且重要的应用前景。

Objective Color recognition of human clothing has become a topic with widespread interest recent years due to its potential to address various issues such as clothing retail,smart security,and fashion recommendation.An accurate color recognition model for human clothing can greatly enhance user experience and service quality.However,accurately recognizing the color of clothing on a human body in images can be challenging due to the multi-angle and multi-pose nature of the clothing,as well as the influence of the material and texture of the clothing,the color and texture of the human body background,and other factors.Therefore,constructing an efficient and accurate human clothing color recognition model is important and necessary to be solved in order to improve recognition accuracy and stability.Method To enable color recognition of human clothing,a human clothing attribute dataset called pcaparsing was first created.Then,an end-to-end convolutional neural network model called ClothResNet was constructed,which used ResNet18 as the backbone network.The model also featured an optimized pyramid pooling module responsible for capturing multi-level semantic information and a coordinate attention mechanism with a focus on the contour information of human clothing.Additionally,atrous convolution was used to improve network efficiency.The dataset was split into training and testing data at an 8∶2 ratio,with 80%of the dataset used for training the ClothResNet model and the remaining 20%used for testing its effectiveness.Comparative experiments were conducted between the traditional clothing color recognition methods of K-means and Hog-KNN,the deep learning clothing color recognition method of CNN,and the method proposed in this paper.Ablation experiments were also conducted to demonstrate the effectiveness of selecting ResNet18 as the backbone network,expanding the dataset,and adding each module to the model.Overall,the study aimed to improve the accuracy and efficiency of color recognition for human clothing.Results This study aimed to recognize the color of human clothing in natural scene images using a convolutional neural network algorithm.The use of proposed network model,ClothResNet,achieved 94.49%accuracy rate in recognizing 12 different colors,demonstrating the feasibility and effectiveness of the deep learning method compared to traditional methods.To evaluate the effectiveness of the ClothResNet model,comparative experiments were conducted with traditional methods,and ablation experiments were designed and carried out.Expanding the dataset significantly improved the evaluation indicators of each network(Tab.1 and Tab.2).Furthermore,the addition of the pyramid pooling block and coordinated attention module further enhanced the performance of the model.These ablation experiments demonstrated the effectiveness of the extended dataset and network modules,laying the foundation for future work on automatic color recognition of human clothing.Overall,this study highlights the potential of deep learning methods for accurately and efficiently recognizing clothing colors in natural scene images.Conclusion In this study,a novel human clothing color recognition model called ClothResNet was proposed,which utilizes ResNet18 as the backbone network and incorporates an improved pyramid pooling module and a coordinate attention mechanism.By combining the strengths of an end-to-end convolutional neural network,this model has led improved recognition of various human clothing colors.Through a series of experiments,we verified the feasibility and effectiveness of our proposed method.This approach provides new ideas for the development of smart clothing,although there is still room for improvement.For instance,there are far more than 12 colors of clothing in reality,so further research is needed to develop methods for recognizing a broader range of colors.