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Hard negative mining is a strategic approach in training SVMs within R-CNN to tackle the predominance of negative samples. This technique specifically targets misclassified negative instances, or false positives, which are challenging for the model to distinguish—these are the "hard negatives".
In object detection algorithms, a "hard negative" refers to a sample that is mistakenly classified by the model as a positive instance—a false positive—despite it not being the target object, but rather background or an unrelated class. Such samples earn the label 'hard' as they bear a strong resemblance to true positives, either visually or contextually, posing a challenge for accurate model classification. If we do not consider hard negative, but randomly sampling from a large pool of negative samples without a focused strategy may lead the model to overlook these hard negatives, failing to compel the model to learn from these challenging but crucial cases.
To implement the hard negative mining, the SVM is initially trained with an equal mix of positive samples and a random sampled negatives. Following this, the model's predictions are analyzed to extract the hard negatives, which are then added back into the negative training samples to partially replace the random samples. This iterative process of retraining the SVM with these challenging cases hones its classification capabilities, leading to a reduction in false positives (hard negative) and a boost in detection accuracy.
Building upon the foundation laid by the original R-CNN, Fast R-CNN introduced significant improvements aimed at addressing some of the inefficiencies and limitations of its predecessor. One of the most notable enhancements was the introduction of a single-stage training process that simultaneously optimizes the classification and bounding box regression tasks. This innovation not only streamlined the training process but also significantly improved the training speed and detection performance.
Term Alert: The candidate box in R-CNN is called the region of interest (RoI) in Fast R-CNN.
Fast R-CNN achieves this efficiency by feeding the entire image to the Convolutional Neural Network just once, and then extracting features to be shared across the different region proposals. This approach contrasts sharply with R-CNN, which processes each candidate region independently, leading to a significant reduction in computational redundancy. Instead of scaling the RoI (the image cropped according to the candidate box) into 224*224, the fast R-CNN model employs a Region of Interest (RoI) pooling layer to extract a fixed-size feature vector from the feature map for each region proposal. These vectors are then fed into a series of fully connected layers that output both the class probabilities and bounding box offsets.
This architecture not only reduces the computational load but also improves detection accuracy by leveraging shared computations. However, Fast R-CNN still relies on selective search for generating RoI proposals, a step that remains a bottleneck in the object detection pipeline. This dependency on an external algorithm for region proposal generation motivates the development of Faster R-CNN, which seeks to integrate this step into the deep learning model itself.
Non-maximum Suppression (NMS, Above Right) is a crucial step in object detection algorithms that helps in reducing the number of redundant bounding boxes around detected objects. When an object detection model predicts multiple bounding boxes for the same object, NMS ensures that only the most probable bounding box is retained, making the detection more accurate and less cluttered.
Faster R-CNN represents a pivotal advancement on top of Fast R-CNN, addressing the primary bottleneck (i.e., the selective search) in Fast R-CNN by incorporating a Region Proposal Network (RPN) that is fully integrated with the detection network. This integration allows for the generation of high-quality region proposals directly within the neural network, effectively making the process nearly real-time. The RPN shares its convolutional layers with the detection network, significantly boosting efficiency by allowing both the proposal generation and object detection tasks to utilize the same feature representations.
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The RPN works by sliding a small network over the feature map obtained from the input image, which then predicts object bounds and objectness scores for each position. These proposals are then refined and classified by the subsequent layers of the network. This approach not only speeds up the detection process but also improves the quality of the region proposals through learning.
Faster R-CNN thus achieves end-to-end training and detection, significantly outperforming its predecessors in both speed and accuracy. This model sets a new standard for object detection frameworks, showcasing the potential of integrating proposal generation with deep learning models for efficient and accurate detection tasks. Despite its advances, the quest for even faster and more accurate models continues, leading to further innovations in the field.