Agile and correct detection of objects is becoming more and more crucial with the increased interest in the area of smart video surveillance, autonomous vehicles, facial recognition, and various distinct applications. These systems not only identify and categorize objects within images and videos but also precisely locate them by describing bounding boxes. This paper administers a detailed analysis of traditional and modern deep learning-based approaches for detection of objects, examining aspects such as multi-scale feature recognition, data augmentation techniques, training methodologies, and viewpoint variability. Key standard datasets utilized in object detection research are also reviewed. Additionally, the paper discusses current challenges and outlines future research directions, particularly focusing on evolving datasets and frameworks that underpin object detection tasks. The analysis reveals that while existing object detection methods perform reasonably well, there remains significant room for improvement, especially in scenarios involving large variations in object scales, occluded views, and challenging environmental conditions. Consequently, the paper suggests avenues for advancement in object detection techniques.