# evaluateDetectionPrecision

(To be removed) Evaluate precision metric for object detection

`evaluateDetectionPrecision`

will be removed in a future release. Use
`evaluateObjectDetection`

instead.

## Syntax

## Description

returns the average precision, of the `averagePrecision`

= evaluateDetectionPrecision(`detectionResults`

,`groundTruthData`

)`detectionResults`

compared to the `groundTruthData`

. You can use the average
precision to measure the performance of an object detector. For a multiclass
detector, the function returns `averagePrecision`

as a vector
of scores for each object class in the order specified by
`groundTruthData`

.

`[`

returns data points for plotting the precision–recall curve, using input
arguments from the previous syntax.`averagePrecision`

,`recall`

,`precision`

]
= evaluateDetectionPrecision(___)

`[___] = evaluateDetectionPrecision(___,`

specifies
the overlap threshold for assigning a detection to a ground truth
box.`threshold`

)

## Examples

### Evaluate Precision of a YOLO v2 Object Detector

This example shows how to evaluate a pretrained YOLO v2 object detector.

**Load the Vehicle Ground Truth Data**

Load a table containing the vehicle training data. The first column contains the training images, the remaining columns contain the labeled bounding boxes.

```
data = load('vehicleTrainingData.mat');
trainingData = data.vehicleTrainingData;
```

Add fullpath to the local vehicle data folder.

dataDir = fullfile(toolboxdir('vision'), 'visiondata'); trainingData.imageFilename = fullfile(dataDir, trainingData.imageFilename);

Create an imageDatastore using the files from the table.

imds = imageDatastore(trainingData.imageFilename);

Create a boxLabelDatastore using the label columns from the table.

blds = boxLabelDatastore(trainingData(:,2:end));

**Load YOLOv2 Detector for Detection**

Load the detector containing the layerGraph for trainining.

```
vehicleDetector = load('yolov2VehicleDetector.mat');
detector = vehicleDetector.detector;
```

**Evaluate and Plot the Results**

Run the detector with imageDatastore.

results = detect(detector, imds);

Evaluate the results against the ground truth data.

[ap, recall, precision] = evaluateDetectionPrecision(results, blds);

Plot the precision/recall curve.

figure; plot(recall, precision); grid on title(sprintf('Average precision = %.1f', ap))

### Evaluate Precision of Stop Sign Detector

Train an ACF-based detector using preloaded ground truth information. Run the detector on the training images. Evaluate the detector and display the precision-recall curve.

Load the ground truth table.

load('stopSignsAndCars.mat') stopSigns = stopSignsAndCars(:,1:2); stopSigns.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ... stopSigns.imageFilename);

Train an ACF-based detector.

`detector = trainACFObjectDetector(stopSigns,'NegativeSamplesFactor',2);`

ACF Object Detector Training The training will take 4 stages. The model size is 34x31. Sample positive examples(~100% Completed) Compute approximation coefficients...Completed. Compute aggregated channel features...Completed. -------------------------------------------- Stage 1: Sample negative examples(~100% Completed) Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 84 negative examples...Completed. The trained classifier has 19 weak learners. -------------------------------------------- Stage 2: Sample negative examples(~100% Completed) Found 84 new negative examples for training. Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 84 negative examples...Completed. The trained classifier has 20 weak learners. -------------------------------------------- Stage 3: Sample negative examples(~100% Completed) Found 84 new negative examples for training. Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 84 negative examples...Completed. The trained classifier has 54 weak learners. -------------------------------------------- Stage 4: Sample negative examples(~100% Completed) Found 84 new negative examples for training. Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 84 negative examples...Completed. The trained classifier has 61 weak learners. -------------------------------------------- ACF object detector training is completed. Elapsed time is 28.8706 seconds.

Create a table to store the results.

numImages = height(stopSigns); results = table('Size',[numImages 2],... 'VariableTypes',{'cell','cell'},... 'VariableNames',{'Boxes','Scores'});

Run the detector on the training images. Store the results as a table.

for i = 1 : numImages I = imread(stopSigns.imageFilename{i}); [bboxes, scores] = detect(detector,I); results.Boxes{i} = bboxes; results.Scores{i} = scores; end

Evaluate the results against the ground truth data. Get the precision statistics.

[ap,recall,precision] = evaluateDetectionPrecision(results,stopSigns(:,2));

Plot the precision-recall curve.

figure plot(recall,precision) grid on title(sprintf('Average Precision = %.1f',ap))

## Input Arguments

`detectionResults`

— Object locations and scores

table

Object locations and scores, specified as a two-column table containing
the bounding boxes and scores for each detected object. For multiclass
detection, a third column contains the predicted label for each detection.
The bounding boxes must be stored in an *M*-by-4 cell
array. The scores must be stored in an *M*-by-1 cell array,
and the labels must be stored as a categorical vector.

When detecting objects, you can create the detection results table by
using `imageDatastore`

.

ds = imageDatastore(stopSigns.imageFilename); detectionResults = detect(detector,ds);

**Data Types: **`table`

`groundTruthData`

— Labeled ground truth

datastore | table

Labeled ground truth, specified as a datastore or a table.

Each bounding box must be in the format [*x*
*y*
*width*
*height*].

Datastore — A datastore whose

`read`

and`readall`

functions return a cell array or a table with at least two columns of bounding box and labels cell vectors. The bounding boxes must be in a cell array of*M*-by-4 matrices in the format [*x*,*y*,*width*,*height*]. The datastore's`read`

and`readall`

functions must return one of the formats:{

*boxes*,*labels*} — The`boxLabelDatastore`

creates this type of datastore.{

*images*,*boxes*,*labels*} — A combined datastore. For example, using`combine`

(`imds`

,`blds`

).

See

`boxLabelDatastore`

.Table — One or more columns. All columns contain bounding boxes. Each column must be a cell vector that contains

*M*-by-4 matrices that represent a single object class, such as*stopSign*,*carRear*, or*carFront*. The columns contain 4-element double arrays of*M*bounding boxes in the format [*x*,*y*,*width*,*height*]. The format specifies the upper-left corner location and size of the bounding box in the corresponding image.

`threshold`

— Overlap threshold

`0.5`

(default) | numeric scalar

Overlap threshold for assigned a detection to a ground truth box, specified as a numeric scalar. The overlap ratio is computed as the intersection over union.

## Output Arguments

`averagePrecision`

— Average precision

numeric scalar | vector

Average precision over all the detection results, returned as a numeric
scalar or vector. *Precision* is a ratio of true
positive instances to all positive instances of objects in the detector,
based on the ground truth. For a multiclass detector, the average precision
is a vector of average precision scores for each object class.

`recall`

— Recall values from each detection

vector of numeric scalars | cell array

Recall values from each detection, returned as an
*M*-by-1 vector of numeric scalars or as a cell array. The
length of *M* equals 1 + the number of detections assigned
to a class. For example, if your detection results contain 4 detections with
class label `'car'`

, then `recall`

contains 5 elements. The first value of recall is always
`0`

.

*Recall* is a ratio of true positive instances to the
sum of true positives and false negatives in the detector, based on the
ground truth. For a multiclass detector, `recall`

and
`precision`

are cell arrays, where each cell contains
the data points for each object class.

`precision`

— Precision values from each detection

vector of numeric scalars | cell array

Precision values from each detection, returned as an
*M*-by-1 vector of numeric scalars or as a cell array. The
length of *M* equals 1 + the number of detections assigned
to a class. For example, if your detection results contain 4 detections with
class label `'car'`

, then `precision`

contains 5 elements. The first value of `precision`

is
always `1`

.

*Precision* is a ratio of true positive instances to
all positive instances of objects in the detector, based on the ground
truth. For a multi-class detector, `recall`

and
`precision`

are cell arrays, where each cell contains
the data points for each object class.

## Version History

**Introduced in R2017a**

### R2023b: `evaluateDetectionPrecision`

will be removed in a future release

The `evaluateDetectionPrecision`

function will be removed in a
future release. Use the `evaluateObjectDetection`

function to evaluate object detection
results with metrics such as the average precision, instead.

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