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基于轻量化YOLOv8s交通标志的检测

Detection of traffic signs based on lightweight YOLOv8s

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  • NETL
  • NSTL
  • 万方数据
为了提高交通标志检测的实时性和可行性,提出了一种基于 YOLOv8s 的轻量化交通标志检测模型.首先,用FasterNet中的残差模块FasterNetBlock替换C2f模块中的BottleNeck,降低模型参数量和计算量;其次,用一种小目标检测层去替换大目标检测层,降低 Backbone 中网络层数,实现大幅度提高检测速度和降低参数量;最后,用Wise-IOU替换原CIOU损失函数,提高速度和精度.在TT100K交通标志数据集上验证,其与YOLOv8s模型比较,mAP50提高了5.16%,参数量降低了76.48%,计算量降低了13.33%,FPS快了 35.83%.与其他模型相比,mAP50 平均提高了 15.11%,参数量平均降低了 85.74%,计算量平均下降了 46.23%,FPS 平均提高了 31.49%.该模型具有检测精度高、参数量少、计算量低、速度快等优点,较原算法有很大地提升,且与其他先进的交通标志检测模型比较时表现出了很强的竞争力,在交通标志检测中具有较大优势.
To enhance the real-time capability and feasibility of traffic sign detection,a lightweight traffic sign detection model based on YOLOv8s was proposed.Firstly,the BottleNeck in the C2f module was replaced with the residual module FasterNetBlock in FasterNet,reducing the model's parameter count and computational complexity.Secondly,the large object detection layer was replaced with a small object detection layer,decreasing the number of network layers in Backbone and achieving a significant improvement in detection speed and a reduction in parameter count.Finally,the original complete intersection over union(CIOU)loss function was replaced with the wise intersection over union(Wise-IOU),thereby enhancing both speed and accuracy.Verified on the TT100K traffic sign dataset,compared with the YOLOv8s model,mAP50 increased by 5.16%,parameter count decreased by 76.48%,computational complexity decreased by 13.33%,and frames per second(FPS)improved by 35.83%.In comparison to other models,mAP50 exhibited an average increase of 15.11%,an average decrease of 85.74%in parameter count,an average decrease of 46.23%in computational complexity,and an average increase of 31.49%in FPS.This model achieved the advantages of high detection accuracy,small number of parameters,low computational complexity,and fast speed.It represented a substantial improvement over the original algorithm and demonstrated strong competitiveness when compared to other advanced traffic sign detection models,with great advantages in traffic sign detection.

lightweightYOLOv8simproved small target layertraffic sign detectionWise-IOUTT100K

朱强军、胡斌、汪慧兰、王杨

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安徽师范大学皖江学院大数据与人工智能系,安徽 芜湖 241000

安徽师范大学物理与电子信息学院,安徽 芜湖 241000

安徽师范大学计算机与信息学院,安徽 芜湖 241000

轻量化 YOLOv8s 改进小目标层 交通标志检测 Wise-IOU TT100K

安徽省高等学校自然科学研究重点项目安徽师范大学皖江学院重点自然科研项目安徽省高等学校省级质量工程项目安徽师范大学皖江学院教学质量工程项目

2023AH052459WJKYZD-2023012022sx052WJXGK-202201

2024

10.11996/JG.j.2095-302X.2024030422

图学学报
中国图学学会

图学学报

CSTPCD北大核心
影响因子：0.73
ISSN：2095-302X
年,卷(期)：2024.45(3)
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