With the rapid advancement of autonomous driving technology, efficient and accurate object detection capabilities have become crucial factors in ensuring the safety and reliability of autonomous driving systems. However, in low-visibility environments such as hazy conditions, the performance of traditional object detection algorithms often degrades significantly, failing to meet the demands of autonomous driving. To address this challenge, this paper proposes two innovative deep learning models: YOLO-Vehicle and YOLO-Vehicle-Pro. YOLO-Vehicle is an object detection model tailored specifically for autonomous driving scenarios, employing multimodal fusion techniques to combine image and textual information for object detection. YOLO-Vehicle-Pro builds upon this foundation by introducing an improved image dehazing algorithm, enhancing detection performance in low-visibility environments. In addition to model innovation, this paper also designs and implements a cloud-edge collaborative object detection system, deploying models on edge devices and offloading partial computational tasks to the cloud in complex situations. Experimental results demonstrate that on the KITTI dataset, the YOLO-Vehicle-v1s model achieved 92.1% accuracy while maintaining a detection speed of 226 FPS and an inference time of 12ms, meeting the real-time requirements of autonomous driving. When processing hazy images, the YOLO-Vehicle-Pro model achieved a high accuracy of 82.3% mAP@50 on the Foggy Cityscapes dataset while maintaining a detection speed of 43 FPS.

翻译：随着自动驾驶技术的快速发展，高效、准确的目标检测能力已成为保障自动驾驶系统安全性与可靠性的关键因素。然而，在雾霾等低能见度环境下，传统目标检测算法的性能往往显著下降，难以满足自动驾驶的实际需求。为应对这一挑战，本文提出了两种创新的深度学习模型：YOLO-Vehicle与YOLO-Vehicle-Pro。YOLO-Vehicle是专为自动驾驶场景定制的目标检测模型，采用多模态融合技术，结合图像与文本信息进行目标检测。YOLO-Vehicle-Pro在此基础上引入改进的图像去雾算法，提升了低能见度环境下的检测性能。除模型创新外，本文还设计并实现了一套云边协同目标检测系统，将模型部署于边缘设备，并在复杂情况下将部分计算任务卸载至云端。实验结果表明，在KITTI数据集上，YOLO-Vehicle-v1s模型在保持226 FPS检测速度与12ms推理时间的同时，准确率达到92.1%，满足自动驾驶的实时性要求。在处理雾霾图像时，YOLO-Vehicle-Pro模型在Foggy Cityscapes数据集上实现了82.3%的mAP@50高精度，同时保持43 FPS的检测速度。