Transparent Peer Review By Scholar9
Object Detection Using Yolo V3
Abstract
Object detection is a vital part of computer vision and has applications in areas such as self-driving cars, surveillance, and augmented reality. YOLOv3 (You Only Look Once) is a significant advancement in real-time object detection known for its speed and accuracy. This document provides a comprehensive overview of YOLOv3, focusing on its architecture, training methods, and performance evaluation.YOLOv3 has introduced several important improvements compared to its previous versions. These enhancements include the use of a more complex backbone network called Darknet-53, the integration of multi-scale feature maps, and upgraded capabilities for predicting bounding boxes and class probabilities using logistic regression.
Shreyas Mahimkar Reviewer
Namaste Sir
Shreyas Mahimkar Reviewer
27 Aug 2024 09:08 AM
Approved
The paper provides a thorough overview of YOLOv3, highlighting its importance in real-time object detection across various applications. The discussion on the advancements introduced by YOLOv3, such as the Darknet-53 backbone and multi-scale feature maps, is well-articulated and demonstrates a clear understanding of the subject. However, the review could benefit from a more detailed comparison with other object detection models to better contextualize YOLOv3's strengths and limitations. Additionally, including some performance metrics or real-world examples would enhance the practical relevance of the content.
IJ Publication Publisher
Thank you.
Shreyas Mahimkar Reviewer