To address the issue of low recognition accuracy of yarn tube types in practical industrial scenarios, this study proposes a SimAM-ResNet18-based image recognition method for cone yarns. Different from the traditional yarn recognition method based on Resnet, the framework introduced in this study combines parameter free attention and swish activation to improve the recognition accuracy and robustness under industrial conditions. First, a high-resolution image acquisition system was designed and implemented. The acquired images were preprocessed using bilateral filtering, Gamma correction, HSI color space extraction, and rapid template matching of edge points to enhance image features. Then, the Swish activation function and SimAM attention mechanism were integrated into the ResNet18 network, effectively improving the model's focus on key regions and its feature representation capabilities. On a dataset composed of 1800 real-world images collected from a textile production line, the proposed model achieved a recognition accuracy of 98.3%, a precision of 0.969, a recall of 0.972, and an F1-score of 0.970, significantly outperforming mainstream models such as MobileNetV2, EfficientNet-B0, and SENet18. Without retraining, the model maintained an accuracy of 92.8% under challenging conditions such as angle variation and illumination changes, demonstrating strong generalization capability and practical industrial value.
Deng et al. (Mon,) studied this question.