Computer vision, a rapidly expanding field, focuses on equipping machines with human-like visual capabilities. One of the key areas of study in computer vision is object detection, which involves localizing and identifying objects. Over time, object detection has seen significant advancements, transitioning from traditional methods to modern deep learning-based approaches. These advancements have brought us closer to achieving near-human levels of accuracy in object detection. Object detection plays a crucial role in a wide range of applications, including face detection, pedestrian detection, autonomous vehicle driving, and more. With its versatility, object detection has proven to be highly effective in various detection tasks. The remarkable progress in object detection owes credit to researchers worldwide who have contributed countermeasures to address the challenges associated with this field. This study aims to shed light on the numerous challenges encountered in object detection. By exploring the insights provided by researchers worldwide, we can gain a comprehensive understanding of the complexities involved in detecting objects and the measures employed to overcome them.