Aerial Object Detection Using Different Models of YOLO Architecture: A Comparative Study

Goyal, Vinat; Singh, Rishu; Kumar, Aveekal; Dhawley, Mrudul; Sharma, Sanjeev

doi:10.1007/978-981-19-2126-1_27

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2024

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Cited by 2 publications

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“…Transfer learning has emerged as one of the go-to methods to adapt models well on a small data set. Deep learning models have outperformed the traditional machine learning models, especially in the computer vision field [12]. YOLOv7 exhibits comparatively better performance as it dynamically learns the class labels through its soft labelling mechanism.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Real-Time Object Detection- A Comparison of YOLO Models

Sharma,

Tyagi,

Dubey

2024

IJIRCST

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Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO variant for gun and weapon detection, providing valuable insights into the strengths and weaknesses of each model for this specific task.

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Section: Literature Reviewmentioning

confidence: 99%