• Sahil Thakur
• Anshul Kalia
• Sumesh Sood

: In the realm of computer vision, object detection encompasses the identification and precise positioning of desired entities within an image or video. With the development of advanced deep learning methodologies, notably in Convolutional Neural Networks (CNNs), Deep learning powered approaches for detection of objects have garnered tremendous acclaim in recent times due to their exceptional capabilities and automatic feature learning capabilities. This study aims to provide a thorough understanding of different One-Stage object detection algorithms, which have shown promising results in various applications. Overall, this study will highlight the strengths and limitations of different single-stage object detection methods, including their architecture, training strategies, and performance on standard benchmarks. By providing a thorough understanding of these methods, this study aims to assist researchers and professionals alike in deciding the appropriate algorithm for their specific application requirements.

Computer Vision (CV), Object Detection (OD), Machine Learning (ML), Deep Learning (DL), CNN’s

"A COMPREHENSIVE ANALYSIS OF ONE-STAGE OBJECT DETECTION MODELS IN COMPUTER VISION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.e722-e733, September-2023, Available :http://www.jetir.org/papers/JETIR2309489.pdf

"A COMPREHENSIVE ANALYSIS OF ONE-STAGE OBJECT DETECTION MODELS IN COMPUTER VISION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppe722-e733, September-2023, Available at : http://www.jetir.org/papers/JETIR2309489.pdf