For training the stop sign detector, only the stop sign ROI labels are needed. The ROI labels for car front and rear must be removed:. Note that there are only 41 training images within this data set. Training an R-CNN object detector from scratch using only 41 images is not practical and would not produce a reliable stop sign detector.
Because the stop sign detector is trained by fine-tuning a network that has been pre-trained on a larger dataset CIFAR has 50, training images , using a much smaller dataset is feasible. The input to this function is the ground truth table which contains labeled stop sign images, the pre-trained CIFAR network, and the training options. The training function automatically modifies the original CIFAR network, which classified images into 10 categories, into a network that can classify images into 2 classes: stop signs and a generic background class. During training, the input network weights are fine-tuned using image patches extracted from the ground truth data.
Positive training samples are those that overlap with the ground truth boxes by 0. Negative training samples are those that overlap by 0 to 0. The best values for these parameters should be chosen by testing the trained detector on a validation set. Ensure that the use of the parallel pool is enabled prior to training. The R-CNN object detector can now be used to detect stop signs in images.
Try it out on a test image:. The R-CNN object detect method returns the object bounding boxes, a detection score, and a class label for each detection. The labels are useful when detecting multiple objects, e. The scores, which range between 0 and 1, indicate the confidence in the detection and can be used to ignore low scoring detections.
The network used within the R-CNN detector can also be used to process the entire test image. By directly processing the entire image, which is larger than the network's input size, a 2-D heat-map of classification scores can be generated. This is a useful debugging tool because it helps identify items in the image that are confusing the network, and may help provide insight into improving training.
Extract the activations from the softmax layer, which is the 14th layer in the network. These are the classification scores produced by the network as it scans the image.
The size of the activations output is smaller than the input image due to the downsampling operations in the network. To generate a nicer visualization, resize stopSignMap to the size of the input image.
This is a very crude approximation that maps activations to image pixels and should only be used for illustrative purposes. The stop sign in the test image corresponds nicely with the largest peak in the network activations. Had there been other peaks, this may indicate that the training requires additional negative data to help prevent false positives.
If that's the case, then you can increase 'MaxEpochs' in the trainingOptions and re-train. Similar steps may be followed to train other object detectors using deep learning. Donahue, T.
Darrell, and J. Columbus, OH, June , pp. Dong, R.
Socher, L. Li, K. Li, and L. This is essential because the next step, feature extraction, is performed on a fixed sized image. The input image has too much extra information that is not necessary for classification. Therefore, the first step in image classification is to simplify the image by extracting the important information contained in the image and leaving out the rest.
For example, if you want to find shirt and coat buttons in images, you will notice a significant variation in RGB pixel values. However, by running an edge detector on an image we can simplify the image.
You can still easily discern the circular shape of the buttons in these edge images and so we can conclude that edge detection retains the essential information while throwing away non-essential information. The step is called feature extraction.
In traditional computer vision approaches designing these features are crucial to the performance of the algorithm. Turns out we can do much better than simple edge detection and find features that are much more reliable. In our example of shirt and coat buttons, a good feature detector will not only capture the circular shape of the buttons but also information about how buttons are different from other circular objects like car tires.
A feature extraction algorithm converts an image of fixed size to a feature vector of fixed size. HOG is based on the idea that local object appearance can be effectively described by the distribution histogram of edge directions oriented gradients. Using the gradient images and , we can calculate the magnitude and orientation of the gradient using the following equations. Histogram of these gradients will provide a more useful and compact representation. We will next convert these numbers into a 9-bin histogram i. The bins of the histogram correspond to gradients directions 0, 20, 40 … degrees.
Every pixel votes for either one or two bins in the histogram. If the direction of the gradient at a pixel is exactly 0, 20, 40 … or degrees, a vote equal to the magnitude of the gradient is cast by the pixel into the bin. A pixel where the direction of the gradient is not exactly 0, 20, 40 … degrees splits its vote among the two nearest bins based on the distance from the bin.
A pixel where the magnitude of the gradient is 2 and the angle is 20 degrees will vote for the second bin with value 2. On the other hand, a pixel with gradient 2 and angle 30 will vote 1 for both the second bin corresponding to angle 20 and the third bin corresponding to angle Block normalization : The histogram calculated in the previous step is not very robust to lighting changes.
Multiplying image intensities by a constant factor scales the histogram bin values as well. To counter these effects we can normalize the histogram — i. The idea is the same, but now instead of a 9 element vector you have a 36 element vector. What is the length of the final vector? Step 3 : Learning Algorithm For Classification In the previous section, we learned how to convert an image to a feature vector.
In this section, we will learn how a classification algorithm takes this feature vector as input and outputs a class label e.
This book contains papers presented at the NATO Advanced Research Workshop on "Real-time Object and Environment Measurement and Classification" held. To build our deep learning-based real-time object detector with .. in studying deep learning for computer vision and image classification tasks.
Before a classification algorithm can do its magic, we need to train it by showing thousands of examples of cats and backgrounds. Although the ideas used in SVM have been around since , the current version was proposed in by Cortes and Vapnik. In the previous step, we learned that the HOG descriptor of an image is a feature vector of length We can think of this vector as a point in a dimensional space.
Visualizing higher dimensional space is impossible, so let us simplify things a bit and imagine the feature vector was just two dimensional. In our simplified world, we now have 2D points representing the two classes e. In the image above, the two classes are represented by two different kinds of dots. All black dots belong to one class and the white dots belong to the other class.
We show the dimensions of the input and output of each network layer which assists in understanding how data is transformed by each layer of the network. Once you understand how training works, understanding inference is a lot easier as it simply uses a subset of the steps involved in training. The goal of training is to adjust the weights in the RPN and Classification network and fine-tune the weights of the head network these weights are initialized from a pre-trained network such as ResNet.
Therefore, to train these networks, we need the corresponding ground truth i. This ground truth comes from free to use image databases that come with an annotation file for each image.