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The You Only Look Once (YOLO) algorithm stands as a cornerstone in the realm of object detection, celebrated for its unparalleled accuracy and efficiency.In this research endeavour, we embark on a comprehensive exploration of the various iterations of the YOLO algorithm.Through meticulous comparative analysis, we unveil the evolutionary trajectory of each YOLO version, shedding light on the motivations behind their respective updates.Our investigation delves deep into the intricacies of target recognition and feature selection methodologies, underscoring the algorithm's continual refinement.Furthermore, this study offers valuable insights into the applications of YOLO in diverse domains, including the financial sector.By elucidating the nuances of YOLO and its counterparts, this paper enriches the discourse surrounding object detection literature.
Maniyar et al. (Mon,) studied this question.