基于改进YOLOv5算法的复杂场景交通目标检测

实时的交通场景目标检测是实现电子监控、自动驾驶等功能的先决条件.针对现有的目标检测算法检测效率不高,以及大多数轻量化目标检测算法模型精度较低,容易误检、漏检目标的问题,本文通过改进YOLOv5目标检测算法来进行模型训练,再使用伪标签策略对训练过程进行优化,然后在KITTI交通目标数据集上将标签合并为3类,对训练出的模型进行测试.实验结果表明,改进的YOLOv5最终模型在该所有类别上的mAP达到了92.5％,对比原YOLOv5训练的模型提高了3％.最后将训练的模型部署到Jetson Nano嵌入式平台上进行推理测试,并通过TensorRT加速推理,测得平均每帧图像的推理时间为77ms,可以实现实时检测的目标.

Traffic Target Detection in Complex Scenes Based on Improved YOLOv5 Algorithm

Real-time target detection in traffic scenarios is the prerequisite of electronic monitoring, automatic driving, and other functions. In view of the low detection efficiency of existing target detection algorithms and the low accuracy of most light target detection algorithms, which are easy to obtain wrong or insufficient target detection, this paper adopts the improved YOLOv5 target detection algorithm for model training, and the pseudo-label strategy for training process optimization. Then, the KITTI traffic target dataset tags are merged into three categories for model training and testing. Through the experimental comparison, the improved YOLOv5 model in this paper achieves 92.5% mAP in all categories, which is 3% higher than the original YOLOv5 training model. Finally, the three categories of the trained models are deployed on the Jetson Nano embedded platform for inference testing, and TensorRT is used to accelerate inference. The average inference time per frame of image is 77ms, which meets the goal of real-time detection.