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Receptive Field Block

A Receptive Field Block is a neural network architectural module designed to enhance feature representations in computer vision tasks by mimicking the structure of receptive fields in the human visual system. It incorporates multi-branch convolutional pathways with varying kernel sizes and applies dilated convolutions with different dilation rates across the branches. By pairing larger kernel sizes with higher dilation rates, the block replicates the biological relationship between receptive field size and eccentricity, which enables the model to capture diverse multi-scale spatial context and expand its effective receptive field without imposing significant computational overhead.

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Receptive Field Block Net for Accurate and Fast Object Detection

Receptive Field Block Net for Accurate and Fast Object Detection

Songtao Liu, Di Huang, Yunhong Wang

OrganizationsAdvanced Innovation Center for Big Data and Brain ComputingBeihang University

Why you should read this

Introduces RFB Net, an object detector that incorporates biologically inspired receptive field structures into lightweight networks to match the accuracy of deep models at real-time speeds.

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at this https URL.

Added

2026-09-24