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Real-time object detection system, single neural network predicts bounding boxes and class probabilities directly from full images, high speed with reasonable accuracy.

Improves upon the original R-CNN by sharing computation for different region proposals, uses ROI pooling to extract features, faster and more efficient than R-CNN.

Extension of Faster R-CNN with pixel-level segmentation, provides high-quality object instance segmentation, more computationally expensive.

Lightweight deep neural networks for mobile and embedded vision applications, use depthwise separable convolutions for efficient performance, suitable for real-time object detection on mobile devices.

Method for detecting objects in images using a single deep neural network, performs well on various object sizes, balances speed and accuracy.

Scalable and efficient object detection model, uses a compound scaling method, achieves higher accuracy with fewer parameters and FLOPS compared to previous models.

A category of methods that remove the need for predefined anchor boxes, detects object centers and directly regresses to bounding boxes, simplifies the detection pipeline.

Employs a focal loss function to address class imbalance during training, achieves a good balance between speed and accuracy, can detect objects at multiple scales.

First framework to apply a deep neural network to object detection, highly accurate but slow due to the separate region proposal and detection stages.