This paper proposes a fast and accurate object detection method called RFB Net, which enhances lightweight CNN features using a hand-crafted mechanism inspired by human visual systems' receptive fields (RFs). The RFB module considers the relationship between RF size and eccentricity to improve feature discriminability and robustness. It is integrated into the SSD framework to create RFB Net, achieving performance comparable to advanced deep detectors while maintaining real-time speed. Experiments on Pascal VOC and MS COCO benchmarks show that RFB Net outperforms existing methods in accuracy and efficiency. The RFB module is generic and can be applied to various network architectures. The method improves feature representation by using multi-branch pooling with varying kernels, dilated convolutions, and reshaping to generate final representations. RFB Net is trained and tested on multiple datasets, demonstrating its effectiveness in real-time object detection. The results show that RFB Net achieves state-of-the-art performance on Pascal VOC and MS COCO while maintaining fast inference speed. The RFB module is also shown to be effective when applied to MobileNet, indicating its generalization ability. The paper also discusses the advantages of RFB over other methods like Inception, ASPP, and Deformable CNN, highlighting its ability to simulate human visual systems' RF properties. The RFB Net is a one-stage detector that achieves high accuracy and speed, making it suitable for real-time applications. The method is efficient and can be applied to lightweight networks, making it suitable for deployment on low-end devices.This paper proposes a fast and accurate object detection method called RFB Net, which enhances lightweight CNN features using a hand-crafted mechanism inspired by human visual systems' receptive fields (RFs). The RFB module considers the relationship between RF size and eccentricity to improve feature discriminability and robustness. It is integrated into the SSD framework to create RFB Net, achieving performance comparable to advanced deep detectors while maintaining real-time speed. Experiments on Pascal VOC and MS COCO benchmarks show that RFB Net outperforms existing methods in accuracy and efficiency. The RFB module is generic and can be applied to various network architectures. The method improves feature representation by using multi-branch pooling with varying kernels, dilated convolutions, and reshaping to generate final representations. RFB Net is trained and tested on multiple datasets, demonstrating its effectiveness in real-time object detection. The results show that RFB Net achieves state-of-the-art performance on Pascal VOC and MS COCO while maintaining fast inference speed. The RFB module is also shown to be effective when applied to MobileNet, indicating its generalization ability. The paper also discusses the advantages of RFB over other methods like Inception, ASPP, and Deformable CNN, highlighting its ability to simulate human visual systems' RF properties. The RFB Net is a one-stage detector that achieves high accuracy and speed, making it suitable for real-time applications. The method is efficient and can be applied to lightweight networks, making it suitable for deployment on low-end devices.