Research on target detection and recognition for autonomous vehicles based on convolutional neural networks

In response to the technical issues of target detection in unmanned vehicles, a precise, comprehensive, and systematic study has been conducted from the perspectives of deep learning algorithms, sensor fusion, and real-time data processing. To address this issue, convolutional neural networks (CNNs), composite communication networks, and integrated sensor technologies were employed, effectively analyzing the unresolved problems, achieving improvements in target detection accuracy, faster system response times, and enhanced environmental adaptability. Simulation experiment results indicate that the target detection accuracy reached 98%, and the response time was reduced to 50 milliseconds, indicating that this technical solution has significant feasibility and effectiveness.