Deep Learning-Based Object Detection in Diverse Weather Conditions
| العنوان: | Deep Learning-Based Object Detection in Diverse Weather Conditions |
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| المؤلفون: | null Ravinder M., Arunima Jaiswal, Shivani Gulati |
| المصدر: | International Journal of Intelligent Information Technologies. 18:1-14 |
| بيانات النشر: | IGI Global, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Decision Sciences (miscellaneous), Information Systems |
| الوصف: | The number of different types of composite images has grown very rapidly in current years, making Object Detection, an extremely critical task that requires a deeper understanding of various deep learning strategies that help to detect objects with higher accuracy in less amount of time. A brief description of object detection strategies under various weather conditions is discussed in this paper with their advantages and disadvantages. So, to overcome this transfer learning has been used and implementation has been done with two Pretrained Models i.e., YOLO and Resnet50 with denoising which detects the object under different weather conditions like sunny, snowy, rainy, and hazy weather. And comparison has been made between the two models. The objects are detected from the images under different conditions where Resnet50 is identified to be the best Model. |
| تدمد: | 1548-3665 1548-3657 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::7d90d01572ed9d585fa47a55861e8827 https://doi.org/10.4018/ijiit.296236 |
| رقم الأكسشن: | edsair.doi...........7d90d01572ed9d585fa47a55861e8827 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15483665 15483657 |
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