Analysis of Object Identification and Classification Using YOLO and RCNN

Through an extensive review of existing literature, the study analyses various findings, highlighting the strengths and weaknesses of both methodologies. This research study compares You Only Look Once (YOLO) and Region-based Convolutional Neural Network (RCNN) in image processing. Focused on accuracy, speed, robustness and performance when integrated with other technologies, the analysis spans various scenarios, revealing YOLO's real-time processing prowess and scalability, contrasting with RCNN's accuracy and adaptability. The research uncovers challenges in object identification techniques, guiding future AI advancements. This research paper is intended to compare the RCNN and YOLO algorithms for object identification and classification in various scenarios such as indoor, outdoor, specialized environments, challenging environments, miscellaneous, and with various real-life objects. It investigates the scenarios in which YOLO or RCNN is more favourable for use.