evaluateDetectionPrecision

(To be removed) Evaluate precision metric for object detection

Use evaluateObjectDetection instead of evaluateDetectionPrecision, which will be removed in a future release. The more recent evaluateObjectDetection can be used to perform a comprehensive analysis of object detector performance.

Syntax

averagePrecision = evaluateDetectionPrecision(detectionResults,groundTruthData)

[averagePrecision,recall,precision] = evaluateDetectionPrecision(___)

[___] = evaluateDetectionPrecision(___,threshold)

Description

averagePrecision = evaluateDetectionPrecision(detectionResults,groundTruthData) returns the average precision, of the 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.

[averagePrecision,recall,precision] = evaluateDetectionPrecision(___) returns data points for plotting the precision–recall curve, using input arguments from the previous syntax.

[___] = evaluateDetectionPrecision(___,threshold) specifies the overlap threshold for assigning a detection to a ground truth box.

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

Use evaluateObjectDetection instead of evaluateDetectionPrecision, which will be removed in a future release. The more recent evaluateObjectDetection can be used to perform a comprehensive analysis of object detector performance.