June 30, 2025JOURNAL OF ICT APLICATIONS AND SYSTEM

Implementation of YOLOv8 for Object Detection in Urban Traffic Surveillance A Case Study on Vehicles and Pedestrians from CCTV Imagery

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YOLOv8 achieves 80% detection accuracy in urban traffic environments, effectively identifying vehicles and pedestrians despite challenges.
Key metrics like precision, recall, and Mean Average Precision (mAP) were used to evaluate the model's performance during trials.
Assessment utilized a 41-second CCTV video dataset, employing deep learning techniques via Google Colab to train the model.
The findings indicate the need for improved dataset diversity and fine-tuning to enhance object detection in dynamic traffic situations.

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Implication

Analysis reveals YOLOv8 achieves 80% accuracy in detecting vehicles and pedestrians from CCTV footage, suggesting its potential in traffic management.

Cite This Study

Saragih et al. (2025) studied this question.

synapsesocial.com/papers/68af2979cf1dd9ea359e310f https://doi.org/10.56313/jictas.v4i1.430

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Implication

Analysis reveals YOLOv8 achieves 80% accuracy in detecting vehicles and pedestrians from CCTV footage, suggesting its potential in traffic management.

Implementation of YOLOv8 for Object Detection in Urban Traffic Surveillance A Case Study on Vehicles and Pedestrians from CCTV Imagery

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Discussion

Implication

Cite This Study

Implementation of YOLOv8 for Object Detection in Urban Traffic Surveillance A Case Study on Vehicles and Pedestrians from CCTV Imagery

Key Points

Discussion

Implication

Cite This Study

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