A Method for Identifying Women's Sleeves Based on Improved YOLOv5 and ResNet50
CAO Hanying, TUO Jiying
2024, 32(1):
45-53.
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In response to the problems of numerous classifications of women's clothing sleeve shapes, difficulty in feature recognition, and unsatisfactory detection results, on the basis of fully utilizing the correlation information between different women's clothing sleeve shapes, an improved YOLOv5 object detection and ResNet50 object classification deep learning method was used to achieve automatic recognition of women's clothing sleeve shapes.
Firstly, two methods of sleeve type classification were combined based on the 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, neck hanging, raglan sleeves, bubble sleeves, and leg sleeves. Secondly, clothing sample images were collected from e-commerce platforms, taking into account factors such as different angles, lighting, and backgrounds. On the basis of balancing different sleeve types, 3600 sleeve type images of women's clothing were collected, screened, and labeled. A clothing sleeve type dataset containing approximately 6500 sleeve type samples was obtained, and sleeve type labeling was performed using Labelimg software. Once again, 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 is proposed for women's sleeve automatic recognition. Among them, 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, 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.
The results indicate that ① compared to 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; ② in the task of identifying women's sleeve type by YOLOv5, improved YOLOv5, YOLOv5 and ResNet50 combined methods, compared with sleeveless and long sleeves, the identification of ultra-short sleeves and short sleeves is more difficult, and the overall accuracy is more difficult to improve.
