Research on the Application of MobileNetV2 Object Detection Algorithm Based on Memristor Cross Array

Background: Memristor-based implementations of Convolutional Neural Networks (CNNs) offer the potential to reduce power consumption and improve computational efficiency significantly. However, the non-ideal characteristics of memristive devices, such as variability and limited switching behavior, can introduce nonlinearity and degrade network performance. It is therefore essential to simulate memristor-based deep learning systems and evaluate the impact of these imperfections before proceeding with hardware-level deployment. Objective: This study aims to design a lightweight memristor-based MobileNetV2 model for smoking behavior detection and analyze the influence of memristor non-idealities on the network’s detection accuracy. Methods: A lightweight MobileNetV2 network was constructed and trained on a self-developed dataset for smoking behavior detection, achieving a peak accuracy of 92.25%. Based on the trained model, a memristor-based MobileNetV2 architecture was implemented using the Mem- Torch simulation framework. Two memristor models, VTEAM and Data-Driven Verilog-A RRAM, were employed to replace the convolutional and fully connected layers with memristor crossbar arrays. The impact of non-ideal memristor properties, including conductance variability and resistance state faults, was systematically evaluated. Results: Simulation results demonstrated that the memristor-based MobileNetV2 network achieved detection accuracies of 92.11% with the VTEAM model and 91.82% with the Verilog-A RRAM model. As device non-idealities increased, the detection accuracy declined significantly, indicating a strong correlation between memristor imperfections and the performance of CNN- based target detection tasks. Conclusion: Memristor non-idealities substantially affect the performance of neuromorphic CNNs. This study provides targeted analyses and proposes mitigation strategies, such as differential encoding and improved fabrication techniques, to support the practical deployment of memristor- based neural networks in energy-efficient AI applications.