Real-time object detection is the task of doing object detection in real-time with fast inference while maintaining a base level of accuracy.
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In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
#5 best model for Real-Time Object Detection on PASCAL VOC 2007
In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
#6 best model for Real-Time Object Detection on PASCAL VOC 2007
A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
#2 best model for Real-Time Object Detection on PASCAL VOC 2007
Here we aim to learn a better architecture of feature pyramid network for object detection.
#10 best model for Real-Time Object Detection on COCO (MAP metric)
Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency.
#6 best model for Real-Time Object Detection on COCO
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.
In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.