Treffer: 基于改进 YOLOv5 和 ResNet50 的女装袖型识别方法.

In response to the problems of numerous classifications of women' s clothing sleeve shapes difficult feature recognition and unsatisfactory detection results a deep learning method based on improved YOLOv5 and ResNet50 was used to achieve automatic recognition of women' s clothing sleeve shapes on the basis of fully utilizing the correlation information between different women' s clothing sleeve shapes. Firstly two methods of sleeve type classification were combined based onthe length and shape of women' s clothing sleeves. The sleeves were divided into four primary classifications based on the length of women' s clothing sleeveless ultra short short and long sleeves. On the basis of the primary classifications they were further divided into 15 secondary classifications based on their morphological characteristics including bra halterneck raglan sleeves puffed sleeves and leg-of-mutton sleeves. Secondly clothing sample images were collected from e-commerce platforms by taking into account factors such as different angles lighting and backgrounds. On the basis of balancing different sleeve types 3 600 sleeve type images of women' s clothing were collected screened and labeled. A clothing sleeve type dataset containing approximately 6 500 sleeve type samples was obtained and sleeve type labeling was performed by using Labelimg software. Thirdly based on the analysis of the YOLOv5 object detection network CBAM attention mechanism ResNet50 residual network principle and network features an improved YOLOv5 and ResNet50 combined deep learning method based on CBAM attention mechanism was proposed for women' s sleeve automatic recognition. Specifically YOLOv5 model gradually adjusts the parameters of the network model through the back propagation and gradient descent characteristics of the convolutional neural network on the self labeled garment sleeve shape data set to obtain the network parameters suitable for the detection of women' s sleeve shape thus realizing the target detection of women' s sleeve shape in the primary level classification. The convolutional attention module CBAM which combines channel attention mechanism and spatial attention mechanism is beneficial for solving the problem of no attention preference in the original network thereby enhancing the effectiveness of sleeve detection. Four independent ResNet50 residual networks were used to carry out sleeve type secondary classification recognition based on the improved YOLOv5 network detection of four sleeve types sleeveless ultra short sleeved short sleeved and long sleeved ones respectively in order to obtain the final results of women' s sleeve type recognition. Finally based on the Python language and Pytorch framework the proposed deep learning algorithm for women' s clothing sleeve recognition was designed and implemented and the model was trained on the sleeve dataset to verify the effectiveness of sleeve recognition through experiments. Two major conclusions are drawn. First compared with the YOLOv5 method and the CBAM improved YOLOv5 method the CBAM improved YOLOv5 and ResNet50 combined method which introduces the correlation information between women' s sleeve shapes has more advantages in the accuracy of women' s sleeve shape recognition. The overall recognition accuracy is about 93. 3 and its overall accuracy is 12. 2 and 8 percentage points higher than the YOLOv5 model improved by YOLOv5 and CBAM respectively. Second in the task of identifying women' s sleeve type by YOLOv5 improved YOLOv5 YOLOv5 and ResNet50 combined methods the identification of ultra-short sleeves and short sleeves is more difficult compared with that of sleeveless and long sleeves and the overall accuracy is more difficult to improve. [ABSTRACT FROM AUTHOR]

针对女装袖型分类繁多, 特征识别困难, 检测效果不理想等问题, 根据不同女装袖型的关联信 息, 结合注意力机制改进的 YOLOv5 目标检测网络和 ResNet50 残差网络, 提出了一种女装袖子造型的自动识 别方法. 首先, 从电商平台收集服装样本图像, 按照长短大类和形态小类标记对女装袖型进行归类, 建立了 包含 3600 张图像的袖型数据集;其次, 结合注意力机制改进的 YOLOv5 目标检测网络和 ResNet50 残差网络, 设计了女装袖型识别方法;最后, 在袖型数据集上开展模型训练, 并通过实验验证袖型识别的效果. 结果表 明:改进 YOLOv5 和 ResNet50 相结合的深度学习方法可以有效地对女装袖型进行识别, 整体识别准确率约 93. 3%. 该女装袖型识别方法准确, 便捷, 可以实现大量服装款式的分类快速检测, 提高服装设计效率, 促进 人工智能技术在服装设计领域的应用, 助力我国智能制造和电子商务的发展。 [ABSTRACT FROM AUTHOR]

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