Traffic signs detection algorithm based on improved YOLOv8

doi:10.13474/j.cnki.11-2246.2024.1217

Abstract

Abstract: It has carried out pilot work on new fundamental surveying and mapping,and has completed more than 10000 km of holographic roads in the city in recent year,covering the main roads in Shanghai.With the rapid development of intelligent driving,accurate detection and identification of road traffic signs is essential in constructing intelligent driving road framework data.In actual scenarios,many factors will bring challenges to the detection and recognition of traffic signs,such as motion blur,sunlight conditions,and shooting angles.So,the paper proposes an improved traffic signs detection algorithm based on YOLOv8.The GAM attention mechanism is introduced in the Neck part of the model to enhance the characteristic information of traffic signs.The Wise_IoU loss function has improved the training performance of the dataset compared to the original loss function.Compared with the model without any optimization,the accuracy and mean average precision increased by 6.5% and 4.1% respectively,which has practical application value.

Key words: high-precision map, intelligent driving, YOLOv8, attention mechanism, loss function, traffic sign detection

CLC Number:

P237

LI Yuting, YUAN Zhenchao, ZHANG Li. Traffic signs detection algorithm based on improved YOLOv8[J]. Bulletin of Surveying and Mapping, 2024, 0(12): 106-110.

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