Qi Wang

Qi Wang, Jingwu Mei

In actual industrial production, the assessment of the steel plate welding effect is an important task, and the segmentation of the weld section is the basis of the assessment. This paper proposes an industrial weld segmentation network based on a deep learning semantic segmentation algorithm fused with heatmap detail guidance and Image Matting to solve the automatic segmentation problem of weld regions. In the existing semantic segmentation networks, the boundary information can be preserved by fusing the features of both high-level and low-level layers. However, this method can lead to insufficient expression of the spatial information in the low-level layer, resulting in inaccurate segmentation boundary positioning. We propose a detailed guidance module based on heatmaps to fully express the segmented region boundary information in the low-level network to address this problem. Specifically, the expression of boundary information can be enhanced by adding a detailed branch to predict segmented boundary and then matching it with the boundary heat map generated by mask labels to calculate the mean square error loss. In addition, although deep learning has achieved great success in the field of semantic segmentation, the precision of the segmentation boundary region is not high due to the loss of detailed information caused by the classical segmentation network in the process of encoding and decoding process. This paper introduces a matting algorithm to calibrate the boundary of the segmentation region of the semantic segmentation network to solve this problem. Through many experiments on industrial weld data sets, the effectiveness of our method is demonstrated, and the MIOU reaches 97.93%. It is worth noting that this performance is comparable to human manual segmentation ( MIOU 97.96%).

Tan Wang, Yun Wei Dong, Tao Zhang et al.

Transformer-based methods have achieved impressive results in time series forecasting. However, existing Transformers still exhibit limitations in sequence modeling as they tend to overemphasize temporal dependencies. This incurs additional computational overhead without yielding corresponding performance gains. We find that the performance of Transformers is highly dependent on the embedding method used to learn effective representations. To address this issue, we extract multivariate features to augment the effective information captured in the embedding layer, yielding multidimensional embeddings that convey richer and more meaningful sequence representations. These representations enable Transformer-based forecasters to better understand the series. Specifically, we introduce Hybrid Temporal and Multivariate Embeddings (HTME). The HTME extractor integrates a lightweight temporal feature extraction module with a carefully designed multivariate feature extraction module to provide complementary features, thereby achieving a balance between model complexity and performance. By combining HTME with the Transformer architecture, we present HTMformer, leveraging the enhanced feature extraction capability of the HTME extractor to build a lightweight forecaster. Experiments conducted on eight real-world datasets demonstrate that our approach outperforms existing baselines in both accuracy and efficiency.