In recent years, the development of UAV technology has provided new perspectives for solving some problems of urban traffic planning. The traffic data collected by the UAV platform are used to identify and analyze vehicles and pedestrians on the ground. Due to the large number of small targets in the UAV, the traditional multi-target detection algorithms have low performance and are prone to leakage and misdetection. In this paper, we propose the ALF-YOLOv8s algorithm to solve these problems. The ALF-YOLOv8s algorithm uses Alterable Kernel Convolution (AKConv) and Efficient Multi-scale Attention (EMA) to improve the feature extraction capability of the algorithm and Large Separable Kernel Attention (LSKA) into SPPF to enhance the semantic fusion between different feature layers The experimental results show that the ALF-YOLOv8s algorithm can detect targets more accurately and comprehensively than the traditional YOLOv8s algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle and Pedestrian Detection for UAV Platform Based on Deep Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Park, Ji Su (editor) / Camacho, David (editor) / Gritzalis, Stefanos (editor) / Park, James J. (editor) / Sha, Ang (author) / Che, Zhiguo (author) / Zang, Xiaolin (author) / Zhao, Wei (author) / Xue, Fuen (author) / Zhang, Yong (author)

    Conference:

    International Conference on Computer Science and its Applications ; 2024 ; Pattaya, Thailand December 18, 2024 - December 20, 2024



    Publication date :

    2025-05-15


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Research on Deep Learning-Based Vehicle and Pedestrian Object Detection Algorithms

    X. Zhang / H. Huang / J. Yang et al. | DOAJ | 2024

    Free access

    Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather

    Zaman, Mostafa / Saha, Sujay / Zohrabi, Nasibeh et al. | IEEE | 2023




    Pedestrian detection using deep learning through a dashcam

    Trivedi, Harshil Pareshkumar | BASE | 2019

    Free access