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Single Shot Detector

A Single Shot Detector is a deep learning framework in computer vision that locates and classifies multiple objects within an image using a single forward pass of a neural network. Unlike two-stage detectors that first generate prospective region proposals and then classify each region in a separate stage, a single shot detector eliminates the proposal step by directly predicting bounding box coordinates and class probabilities simultaneously. It applies convolutional filters across multiple feature map layers at varying scales, enabling the network to evaluate predefined default anchor boxes of diverse aspect ratios and sizes. This multi-scale approach allows the model to detect both small and large objects efficiently, offering a strong balance between real-time inference speed and competitive detection accuracy.

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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