To enhance the accuracy and stability of conductive particle detection and to meet actual production 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 introduced 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 network. 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 algorithm 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, respectively, 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 detection performance under adhesion interference, meeting industrial requirements for accuracy, stability, and real-time processing.