An efficient novel paradigm for object detection through web camera using deep learning (YOLOv5’s object detection model)
نویسندگان
چکیده
Object detection, a fundamental duty in computer vision that has wide range of practical applications, they are surveillance, robotics, and autonomous driving. Recent developments deep learning have got gradual improvemenrts detection accuracy speed. One the most popular effective models for object is YOLOv5. In this discussion, we an model through YOLOv5 its implementation tasks. We discuss model’s architecture, training process, evaluation metrics. Furthermore, present experimental results on benchmarks to demonstrate efficacy efficiency detecting various objects complex scenes. Our experiments states out performs other state art case detected image speed making it promising approach real-world applications. work contributes growing body research learning-based provides valuable insights into capabilities limitations By improving accuracy, models, enabled applications can benefit society countless ways.
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ژورنال
عنوان ژورنال: E3S web of conferences
سال: 2023
ISSN: ['2555-0403', '2267-1242']
DOI: https://doi.org/10.1051/e3sconf/202339101093