Detection of conductive multi-particles based on circular convolutional neural network
To enhance the accuracy and stability of conductive particle detection and to meet actual produc-tion demands,a multi-particle detection method based on a simplified deformable convolutional(circular convolutional)neural network is proposed.First,an appropriate model and network are chosen based on the characteristics of the detection task and target.Then,a deformable convolution sampling strategy is in-troduced and modified to restrict the sampling point offset,with added size control parameters.A circular convolution,more suitable for particle detection,replaces some convolutional layers of the original net-work.Additionally,an attention mechanism is introduced to calculate self-attention through label graphs,which serve as weight modification loss functions and label graphs.Finally,a comprehensive evaluation al-gorithm for the accuracy and stability of repeatability and reproducibility indicators is proposed.The results show that the repeatability and reproducibility indicators of our method are 0.809 2 and 0.705 1,respective-ly,outperforming existing mainstream methods by 4.52%and 1.74%.The accuracy and recall rates are 0.712 8 and 0.697 4,respectively,with an overall accuracy of 0.834 1,surpassing existing methods by 1.68%.Compared to existing mainstream methods,our approach significantly improves the particle detec-tion performance under adhesion interference,meeting industrial requirements for accuracy,stability,and real-time processing.
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