Abstract:

Laser coding on metal surface can lead to denaturation of surrounding metal and generate a significant amount of noise in the form of burns. This results in complex backgrounds in the character region, low contrast, and ambiguity of characters, which can make subsequent character recognition challenging. In response, Res18-UNet, a novel laser coding character feature enhancement and fine segmentation model, was proposed. The proposed model was based on residual and feature-grouped attention to highlight character information and improve signal-to-noise ratio, thus effectively segmenting the target. Firstly, the A-R unit was designed to reduce network parameters, effectively avoid network degradation, and improve the feature selection ability in channels and spaces. Secondly, the feature-grouped attention mechanism was proposed, and the improved spatial attention was added to enhance weak character features. In addition, a deep supervision module integrating the improved loss function was designed in the upsampling stage to improve network convergence and enhance segmentation precision. According to the experiment on the image dataset of the can bottoms with laser coding, the proposed model outperformed the original UNet model in terms of mIoU, Dice coefficient, and F1 score. Specifically, the proposed model achieved 0.801 0, 0.889 5, and 0.903 5, respectively, and attained the prediction speed 2.6 times that of the original UNet at 12.24 images/s. Experiments have proven that this algorithm can effectively enhance the features of low contrast laser coded characters and segment them with high precision, and that it has the feasibility and application prospect of deployment and operation on embedded platforms.

Key words: laser coding characters, image segmentation, spatial attention mechanism, residual neural network, feature group strategy