R-CNN (Region-based Convolutional Neural Networks)
Region-based Convolutional Neural Networks (R-CNNs) can be one of the approaches to utilize deep learning in object detection tasks.R-CNNs propose (propose) regions in which objects exist, and then process those regions individually using convolutional Neural Networks (CNN) described in “Overview of CNN and examples of algorithms and implementations” to process them and predict object classes and bounding boxes, and R-CNNs have shown very good performance in object detection tasks.
The main elements of the R-CNN architecture are as follows
1. Region Proposal:
The first step is to propose regions in the image where objects are most likely to be present. These proposals are generated using conventional methods (e.g., Selective Search, EdgeBoxes) to narrow down the regions that are most likely to contain the object to be detected.
2. feature extraction:
Image patches (small image areas) within each proposed region are cropped and transformed into feature maps using CNN. This extracts the features within each proposed region. At this stage, convolutional and pooling layers are used to generate the feature maps.
3. classification and bounding box regression:
The feature maps are used to classify the object classes within the proposed regions and to regress the bounding boxes of the objects. Typically, a softmax activation function is used to classify object classes, and the coordinates of the bounding box (coordinate regression) are predicted via a regression network.
4. nms (non-maximum suppression):
NMS is applied to remove duplicate object detections from the proposed bounding boxes. This ensures that only the most confident object detections are retained.
The main advantage of R-CNNs will be that they provide high detection accuracy. However, R-CNNs have the disadvantages of high computational cost and slow object detection speed. To solve this problem, fast and efficient object detection models based on R-CNNs have been developed, such as Fast R-CNN described in “Overview of Faster R-CNN, Algorithms, and Examples of Implementation“, and the latest EfficientDet described in “Overview of EfficientDet, Algorithms and Examples of Implementation“. These models have improved the efficiency of the region proposal process and feature extraction, enabling object detection in real time.
Specific procedures for R-CNN (Region-based Convolutional Neural Networks)
Region-based Convolutional Neural Networks (R-CNN) is a method for performing object detection tasks and will use a combination of region suggestion and deep learning. The specific steps of R-CNN are described below. 1.
1. Region Proposal:
Region Proposal: Propose regions in the image where objects are likely to be present, usually using algorithms such as Selective Search described in “”Overview of Selective Search and Examples of Algorithms and Implementations” and EdgeBoxes described in “Overview of EdgeBoxes Algorithm and Examples of Implementations.
2. feature extraction:
For each proposed region, a feature map is extracted using a CNN. The image patches in the proposed regions are the input to the CNN, and the features are computed by the convolutional and pooling layers. This feature map is used for object classification and location regression in subsequent steps.
3. Object Classification:
For each proposed region, a neural network is applied to classify the object class. Typically, an all-coupling layer (tightly coupled layer) is used to calculate the confidence (score) for each class, and the class with the highest confidence is selected using a softmax activation function.
4. bounding box regression:
For each proposed region, another neural network is applied to regress the coordinates of the bounding box of the object. This predicts the exact location of the object within the proposed region. Typically, a linear activation function is used in the output layer for the regression.
5. non-maximum suppression (NMS):
Based on the classification score, non-maximum suppression (NMS) is applied to remove overlapping object detections; NMS keeps the object detection with the highest confidence from the overlapping bounding boxes and removes other object detections.
6. display of detection results:
Finally, the detection results are displayed for the proposed region where the object class and location are predicted. This is usually done by drawing a bounding box on the original image and displaying the object class name and confidence level.
Although R-CNN performs well in the object detection task, it is characterized by high computational cost. For this reason, research is underway to make R-CNN faster and more efficient in subsequent models.
Examples of R-CNN (Region-based Convolutional Neural Networks) implementations
Although the implementation of R-CNN is relatively complex and it is difficult to provide a complete implementation code, a simple example implementation using Python and the Deep Learning library Keras is provided to demonstrate the basic concepts of R-CNN. Actual object detection tasks will require more advanced models and datasets.
The following will be a sample code showing the basic steps of R-CNN. The code generates proposed regions of an object and processes those regions with a convolutional neural network (CNN) as described in “About ResNet (Residual Network)” to perform object class classification and bounding box regression.
import numpy as np
import keras
from keras.applications import ResNet50
from keras.layers import Input, Flatten, Dense
from keras.models import Model
from keras.optimizers import SGD
from skimage import io
from skimage.transform import resize
from selective_search import selective_search # Region Proposal Algorithm
# Loading Images
image_path = 'image.jpg'
image = io.imread(image_path)
# Generation of area proposals
regions = selective_search(image, mode='quality', random_sort=False)
# Number of selected areas
num_regions = 1000
regions = regions[:num_regions]
# Extract features in the selected region
roi_features = []
for region in regions:
x, y, w, h = region['rect']
roi = image[y:y+h, x:x+w]
roi = resize(roi, (224, 224)) # Resize to ResNet50 input size
roi_features.append(roi)
roi_features = np.array(roi_features)
# Load ResNet50 model (used for feature extraction)
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
flatten = Flatten()(base_model.output)
fc_layers = Dense(4096, activation='relu')(flatten)
fc_layers = Dense(4096, activation='relu')(fc_layers)
predictions = Dense(num_classes, activation='softmax')(fc_layers)
model = Model(inputs=base_model.input, outputs=predictions)
# feature extraction
roi_features = base_model.predict(roi_features)
# Set up models for class classification and bounding box regression
classification_model = Model(inputs=base_model.input, outputs=predictions)
bbox_regression_model = Model(inputs=base_model.input, outputs=fc_layers)
# Compile Model
classification_model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.001, momentum=0.9), metrics=['accuracy'])
bbox_regression_model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.001, momentum=0.9), metrics=['mean_squared_error'])
# Input feature maps into the classification model to predict classes
class_scores = classification_model.predict(roi_features)
# Input feature maps into bounding box regression model to predict bounding boxes
bounding_box_predictions = bbox_regression_model.predict(roi_features)Challemge for R-CNN (Region-based Convolutional Neural Networks)
While Region-based Convolutional Neural Networks (R-CNNs) have high performance in object detection, there are several challenges and limitations. The main challenges of R-CNNs are described below.
1. high computational cost:
R-CNNs are computationally very expensive because they generate region proposals and process each proposal using a convolutional neural network. For this reason, it is not suitable for real-time object detection. In contrast, improved models (Fast R-CNN, Faster R-CNN, EfficientDet, etc.) described below address this issue and achieve fast object detection.
2. overgeneration of region proposals:
In R-CNN, many proposals are generated by the region proposal algorithm. This generates a lot of redundant computations and reduces efficiency. The improved model reduces the overgeneration of proposals and improves computational efficiency while maintaining accuracy.
3. lack of feature sharing:
In R-CNN, feature extraction is performed for each proposal, and a feature sharing mechanism is lacking. This leads to waste of computational resources and duplication of features, resulting in reduced efficiency. The improved model achieves feature sharing and improves computational efficiency.
4. data bias:
Data may be biased against some classes, especially when training on large data sets. This may degrade detection performance for some classes, requiring data expansion and balanced data collection.
5. constraints on object scale and rotation:
R-CNNs are constrained with respect to object scale and rotation. Optimization for a particular scale and rotation in training data leads to poor performance for other scales and rotations. To address this, multi-scale approaches and data augmentation are used.
6. accuracy of region suggestion:
The region suggestion algorithm is not perfect and may generate incorrect suggestions. This can increase false positives and degrade performance.
R-CNN (Region-based Convolutional Neural Networks)の課題への対応策
The following methods are being considered to address the challenges of Region-based Convolutional Neural Networks (R-CNNs).
1. reduction of computational cost:
One of the biggest challenges of R-CNNs is the high computational cost. To address this issue, fast object detection models based on Fast R-CNN and R-CNN have been developed, as described in “Overview of Fast R-CNN and Examples of Algorithms and Implementations“. These models share the region proposal generation and feature extraction to streamline the process.
2. region proposal reduction:
Methods have been proposed to reduce the number of suggestions generated by the region suggestion algorithm. For example, the Selective Search described in “Overview of Selective Search, Algorithm, and Example Implementation” and the EdgeBoxes described in “Overview of EdgeBoxes Algorithm and Example Implementation” are high-quality algorithms that reduce the number of region proposals.
3 Feature Sharing:
In R-CNN, each proposed feature is shared by all the proposed features. While feature extraction is performed for each proposal in R-CNN, feature sharing is introduced in Fast R-CNN and Faster R-CNN to improve efficiency of computational resources. Such models reduce the computational cost because the features of the entire image are computed only once through the CNN.
4. data balancing:
Oversampling and undersampling may be performed to resolve class imbalances in the data set. Also, a technique called hard negative mining can be used to focus on difficult negative samples. For more information on these data-balancing issues, see “Dealing with Machine Learning with Inaccurate Supervisory Data.
5. addressing scale and rotation:
To alleviate constraints on scale and rotation, use a multi-scale approach or data expansion. This allows us to train models that are robust to various scales and rotations of the object. See also “Small Data Machine Learning Approaches and Examples of Various Implementations” for more details.
6. accurate region suggestion:
False positives can be reduced by improving region suggestion algorithms and using more accurate suggestion generation methods.
Combined, these measures can improve the performance of R-CNN-based object detection models and reduce computational cost. It is also important to consider modern object detection models (e.g., YOLO, SSD, EfficientDet, etc.) as alternatives to R-CNNs, which provide fast and efficient object detection and are suitable for a variety of tasks.
Reference Information and Reference Books
For details on image information processing, see “Image Information Processing Techniques.
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