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vehicleDetectorFasterRCNN

Detect vehicles using Faster R-CNN

Description

example

detector = vehicleDetectorFasterRCNN returns a trained Faster R-CNN (regions with convolution neural networks) object detector for detecting vehicles. Faster R-CNN is a deep learning object detection framework that uses a convolutional neural network (CNN) for detection.

The detector is trained using unoccluded images of the front, rear, left, and right sides of vehicles. The CNN used with the vehicle detector uses a modified version of the MobileNet-v2 network architecture.

Use of this function requires Deep Learning Toolbox™.

Note

The detector is trained using uint8 images. Before using this detector, rescale the input images to the range [0, 255] by using im2uint8 or rescale.

Examples

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Detect cars in a single image and annotate the image with the detection scores. To detect cars, use a Faster R-CNN object detector that was trained using images of vehicles.

Load the pretrained detector.

fasterRCNN = vehicleDetectorFasterRCNN;

Use the detector on a loaded image. Store the locations of the bounding boxes and their detection scores.

I = imread('highway.png');
[bboxes,scores] = detect(fasterRCNN,I);

Annotate the image with the detections and their scores.

I = insertObjectAnnotation(I,'rectangle',bboxes,scores);
figure
imshow(I)
title('Detected Vehicles and Detection Scores')

Figure contains an axes object. The axes object with title Detected Vehicles and Detection Scores contains an object of type image.

Output Arguments

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Trained Faster R-CNN-based object detector, returned as an fasterRCNNObjectDetector object.

Compatibility Considerations

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Behavior change in future release

Introduced in R2017a