针对烟叶非法运输监管中“复杂场景检测精度不足、资源受限、环境实时性差、车辆溯源能力弱”的问题,本研究构建“检测-识别-部署”多模型协同优化体系,以改进高精度检测模块RT-DETR（RT-DETR-R18-P2）解决复杂场景高精度检测,改进属性识别模块ResNet34（ResNet34-SE）实现车辆属性溯源,YOLO系列模型（YOLOv5/v8）适配资源受限部署,通过“功能分工-目标协同-动态调度”实现优势互补。以云南曲靖烟叶卡口3580张车辆图像验证：协同体系复杂场景mAP50达92.8%,边缘设备推理速度为150FPS,属性识别准确率达93.8%,形成“全场景覆盖、全资源适配”的智能监管技术方案,为烟叶非法运输防控提供技术支撑。

In response to the problems of insufficient detection accuracy in complex scenarios, poor real-time performance in resource constrained environments, and weak vehicle traceability capabilities in the supervision of illegal tobacco transportation, this study constructs a "detection recognition deployment" multi-model collaborative optimization system: improving RT-DETR (RT-DETR-R18-P2) to solve high-precision detection in complex scenarios, improving ResNet34 (ResNet34-SE) to achieve vehicle attribute traceability, and adapting YOLO series models (YOLOv5/v8) to resource constrained deployment, achieving complementary advantages through "functional division-target collaboration-dynamic scheduling". Verified by 3580 vehicle images from the tobacco checkpoint in Qujing, Yunnan, the mAP50 of the collaborative system in complex scenes reached 92.8%, the inference speed of edge devices was 150 FPS, and the accuracy of attribute recognition was 93.8%. This formed an intelligent supervision scheme with "full scene coverage and full resource adaptation", providing technical support for the prevention and control of illegal tobacco transportation.