面向交通标志的Ghost-YOLOv8检测算法

doi:10.3778/j.issn.1002-8331.2306-0032

摘要/Abstract

摘要： 针对当前传统网络模型对交通标志识别精度低、检测不准确的问题，提出一种基于YOLOv8n优化、改进的Ghost-YOLOv8交通标志检测模型。使用GhostConv代替部分Conv，设计全新的C2fGhost模块代替部分C2f，减少了模型的参数量，提升了模型的检测性能；在Neck部分添加GAM注意力机制模块，强化特征中的语义信息和位置信息，提高了模型的特征融合能力；针对检测小目标时尺度不一导致语义信息的丢失，添加小目标检测层，增强深层语义信息与浅层语义信息的结合；使用GIoU边界损失函数代替原损失函数，提升了网络的边界框回归性能。实验结果表明，改进的模型在中国交通标志检测数据集TT100K中的精确度（Precision）及平均精度均值（mAP）相较于原模型分别提高了9.5、6.5个百分点，模型的参数量及模型大小相比原模型分别降低了0.223×109、0.2?MB。综合说明，该模型在减少模型参数量及大小的同时提高了检测精度，显著优于对比算法，也满足边缘计算设备的要求，具有实际的应用价值。

关键词: YOLOv8, 交通标志, GhostNet, 全局注意机制（GAM）, 小目标检测层, GIoU

Abstract: Aiming at the problems of low accuracy and inaccurate detection of traffic signs in the current traditional network model, a Ghost-YOLOv8 traffic sign detection model is proposed based on YOLOv8n optimization and improvement. First of all, using GhostConv instead of some Conv, designing a new C2fGhost module instead of some C2f, the model’s parameters is reduced and the detection performance of the model is enhanced. Secondly, a GAM attention mecha-
nism module is added to the Neck part to strengthen the semantic and positional information in the features, which improves the feature fusion ability of the model; then, for detecting the loss of semantic information when detecting small targets, it adds a small target detection layer to enhance the combination of deep semantic information and shallow semantic information. Finally, it uses the GIoU boundary border loss function to replace the original loss function, which improves the backbone of the network’s boundary frame. The experimental results show that the accuracy（Precision） and average accuracy average（mAP） of the improvement model in China traffic sign detection data set TT100K are increased by 9.5 and 6.5 percentage points compared with the original model. And the reduction of number of model parameters and model size are 0.223×109 and 0.2 MB, respectively. Comprehensive explanation, the model of this article improves the detection accuracy while reducing the amount and size of the model, which is significantly better than the comparison algorithm, and also meets the requirements of the edge computing equipment, and has practical application value.

Key words: YOLOv8, traffic signs, GhostNet, global attention mechanism（GAM）, small target detection layer, GIoU

熊恩杰, 张荣芬, 刘宇红, 彭靖翔. 面向交通标志的Ghost-YOLOv8检测算法[J]. 计算机工程与应用, 2023, 59(20): 200-207.

XIONG Enjie, ZHANG Rongfen, LIU Yuhong, PENG Jingxiang. Ghost-YOLOv8 Detection Algorithm for Traffic Signs[J]. Computer Engineering and Applications, 2023, 59(20): 200-207.

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