A domain adversarial graph convolutional network for intelligent monitoring of tool wear in machine tools

Li, Kai; Li, Zhoulong; Jia, Xianshi; Liu, Lei; Chen, Mingsong

doi:10.1016/j.cie.2023.109795

Cited by 8 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the current double decoupled head can alleviate the conflicts and redundant information to improve accuracy, it will lead to a great increase in model complexity [20]. Notably, the emerging graph convolutional network (GCN) [21] can improve the ability to read and recognize surface defect features, as it can compensate for the limitations of CNN in dealing with graph structures in Euclidean space [22]. Hence, this study aims to enhance the detection head's location and recognition capabilities by incorporating GCN.…”

Section: Introductionmentioning

confidence: 99%

High-accuracy and lightweight weld surface defect detector based on graph convolution decoupling head

Wang,

Chen,

Lin

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

The essence of the difficulties for weld surface detection is that there is a lot of interference information during detection. This study aims to enhance the detection accuracy while keeping great deployment capabilities of a detection model for weld surface defects. To achieve this goal, an improved Yolo-GCH model is proposed based on the stable and fast Yolo-v5. The improvements primarily involve introducing a graph convolution network combined with a self-attention mechanism in the head part (i.e., GCH). This component focuses on improving the insufficient recognition capability of CNN for similar defects in complex environments. Furthermore, to address the presence of potentially ambiguous samples in complex welding environments, the label assignment strategy of simOTA is implemented to optimize the anchor frame. Additionally, a streamlined structure, aiming to improve model detection speed while minimizing performance impact, has been designed to enhance the applicability of the model. The results demonstrate that the cooperation of GCH and simOTA significantly improves the detection performance while maintaining the inference speed. These strategies lead to a 2.5% increase in mAP@0.5 and reduce the missing detection rates of weld and 8 types of defects by 32.9% and 84.1% respectively, surpassing other weld surface detection models. Furthermore, the impressive applicability of the model is verified across four scaled versions of Yolo-v5. Based on the proposed strategies, the FPS increases by more than 30 frames in the fast s and n versions of Yolo-v5. These results demonstrate the great potential of the model for industrial applications.

show abstract

Section: Introductionmentioning

confidence: 99%