This paper focuses on real-time object detection systems,  analyzing existing Field-Programmable Gate Arrays (FPGAs) implementations that aim to achieve the best efficiency,  performance, and accuracy at the same time. These three metrics are typically crucial for domains such as autonomous driving, and robotics. Fortunately, recent advancements in object detection models, particularly based on Convolutional Neural Networks (CNNs), have significantly improved object detection accuracy and speed. When these models are combined with FPGAs, it is possible to achieve even more energy efficiency and more easily satisfy real-time constraints. FPGAs can deliver low latency and high throughput by leveraging true parallelism making them suitable platforms for developing real-time object detection systems. This paper reviews existing literature on FPGA-based real-time object detection, discussing commonly used algorithms, acceleration techniques, and optimization strategies. Evaluation metrics and typical datasets for assessing real-time systems are also examined.We have compared the performance of these implementations by using pixel throughput as a fair metric across different systems while processing video streams or images. Insights into state-of-the-art works, comparative analysis, challenges, and future research directions are provided to guide researchers interested in leveraging FPGA devices for real-time object detection applications.