Research on Traffic Sign Detection Based on Improved YOLOv8
Zhongjie Huang, Lintao Li, Gerd Christian Krizek, Linhao Sun
Abstract
Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher.
Topics & Concepts
Pyramid (geometry)Computer scienceFeature (linguistics)Artificial intelligenceTraffic signSign (mathematics)Pattern recognition (psychology)Key (lock)Computer visionNoise (video)Traffic sign recognitionLayer (electronics)Image (mathematics)MathematicsComputer securityChemistryLinguisticsMathematical analysisPhilosophyOrganic chemistryGeometryAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect DetectionAdvanced Image and Video Retrieval Techniques