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Enhanced YOLOv8n model boosts real-time vehicle detection with attention and efficient convolution

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an improved YOLOv8n model for real-time vehicle detection, incorporating Ghost Modules, CBAM, and DCNv2. This enhanced model aims to boost performance in intelligent transportation systems by reducing feature redundancy and refining feature representation. Tested on the KITTI dataset, the model achieved a 95.4% [email protected], an improvement of nearly 9% over the standard YOLOv8n. AI

IMPACT Offers a more accurate and efficient solution for vehicle detection in intelligent transportation systems.

RANK_REASON This is a research paper detailing an improved computer vision model for a specific application.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Enhanced YOLOv8n model boosts real-time vehicle detection with attention and efficient convolution

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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Syed Sajid Ullah, Muhammad Zunair Zamir, Ahsan Ishfaq, Salman Khan ·

    Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems

    arXiv:2604.22856v1 Announce Type: new Abstract: Accurate vehicle detection is a critical component of autonomous driving, traffic surveillance, and intelligent transportation systems. This paper presents an enhanced YOLOv8n-based model that integrates the Ghost Module, Convolutio…

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