evaluateDetectionAOS
(To be removed) Evaluate average orientation similarity metric for object detection
Since R2020a
evaluateDetectionAOS
will be removed in a future release. Use evaluateObjectDetection
instead.
Syntax
Description
computes the average orientation similarity (AOS) metric. The metric can be used to measure
the detection results metrics
= evaluateDetectionAOS(detectionResults
,groundTruthData
)detectionResults
against ground truth data
groundTruthData
. The AOS is a metric for measuring detector
performance on rotated rectangle detections.
additionally specifies the overlap threshold for assigning a detection to a ground truth
bounding box.metrics
= evaluateDetectionAOS(detectionResults
,groundTruthData
,threshold
)
Examples
Evaluate Rotated Rectangle Detections
Define ground truth bounding boxes for a vehicle class. Each row defines a rotated bounding box of the form [xcenter, ycenter, width, height, yaw].
gtbbox = [
2 2 10 20 45
80 80 30 40 15
];
gtlabel = "vehicle";
Create a table to hold the ground truth data.
groundTruthData = table({gtbbox},'VariableNames',gtlabel)
groundTruthData=table
vehicle
____________
{2x5 double}
Define detection results for rotated bounding boxes, scores, and labels.
bbox = [ 4 4 10 20 20 50 50 30 10 30 90 90 40 50 10 ]; scores = [0.9 0.7 0.8]'; labels = [ "vehicle" "vehicle" "vehicle" ]; labels = categorical(labels,"vehicle");
Create a table to hold the detection results.
detectionResults = table({bbox},{scores},{labels},'VariableNames',{'Boxes','Scores','Labels'})
detectionResults=1×3 table
Boxes Scores Labels
____________ ____________ _________________
{3x5 double} {3x1 double} {3x1 categorical}
Evaluate the detection results against ground truth by calculating the AOS metric.
metrics = evaluateDetectionAOS(detectionResults,groundTruthData)
metrics=1×5 table
AOS AP OrientationSimilarity Precision Recall
______ _______ _____________________ ____________ ____________
vehicle 0.5199 0.54545 {4x1 double} {4x1 double} {4x1 double}
Input Arguments
detectionResults
— Detection results
three-column table
Detection results, specified as a three-column table. The columns contain rotated rectangle bounding boxes, scores, and labels.
Bounding Box | Format | Description |
---|---|---|
Rotated rectangle | [xcenter, ycenter, width, height, yaw] | This type of bounding box is defined in spatial coordinates as an M-by-5 matrix representing M bounding boxes. The xcenter and ycenter coordinates represent the center of the bounding box. The width and height elements represent the length of the box along the x and y axes, respectively. The yaw represents the rotation angle in degrees. The amount of rotation about the center of the bounding box is measured in the clockwise direction. |
groundTruthData
— Labeled ground truth images
datastore | table
Labeled ground truth images, specified as a datastore or a table.
If you use a datastore, your data must be set up so that calling the datastore with the
read
andreadall
functions returns a cell array or table with two or three columns. When the output contains two columns, the first column must contain bounding boxes, and the second column must contain labels, {boxes,labels}. When the output contains three columns, the second column must contain the bounding boxes, and the third column must contain the labels. In this case, the first column can contain any type of data. For example, the first column can contain images or point cloud data, such as:data boxes labels The first column must be images.
Bounding boxes The third column must be a cell array that contains M-by-1 categorical vectors containing object class names. All categorical data returned by the datastore must contain the same categories.
If you use a table, the table must have two or more columns.
data boxes ... The first column can contain data, such as point cloud data or images. Each of the remaining columns must be a cell vector that contains M-by-5 matrices representing rotated rectangle bounding boxes. Each rotated rectangle must be of the form[xcenter, ycenter, width, height, yaw]. The vectors represent the location and size of bounding boxes for the objects in each image.
threshold
— Overlap threshold
nonnegative scalar
Overlap threshold, specified as a nonnegative scalar. The overlap ratio is defined as the intersection over union.
Output Arguments
metrics
— AOS metrics
five-column table
AOS metrics, returned as a five-column table. Each row in the table contains the
evaluation metrics for a class which is defined in the ground truth data contained in
the groundTruthData
input. To get the object class
names:
metrics.Properties.RowNames
metrics
table.
Column | Description |
---|---|
AOS | Average orientation similarity value |
AP | Average precision over all the detection results, returned as a numeric scalar. Precision is a ratio of true positive instances to all positive instances of objects in the detector, based on the ground truth. |
OrientationSimilarity | Orientation similarity values for each detection, returned as an
M-element numeric column. M is one
more than the number of detections assigned to a class. The first value of
Orientation similarity is a normalized variant of the cosine similarity that measures the similarity between the predicted rotation angle and the ground truth rotation angle. |
Precision | Precision values from each detection, returned as an
M-element numeric column vector. M
is one more than the number of detections assigned to a class. For example,
if your detection results contain 4 detections with class label 'car', then
Precision is a ratio of true positive instances to all positive instances of objects in the detector, based on the ground truth. |
Recall | Recall values from each detection, returned as an
M-element numeric column vector. M
is one more than the number of detections assigned to a class. For example,
if your detection results contain 4 detections with class label 'car', then
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. |
References
[1] Geiger, A., P. Lenz., and R. Urtasun. "Are we ready for autonomous driving? The KITTI vision benchmark suite." IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012.
Version History
Introduced in R2020aR2023b: evaluateDetectionAOS
will be removed in a future release
The evaluateDetectionAOS
function will be removed in a future
release. Use the evaluateObjectDetection
function to evaluate object detection results with
metrics such as the AOS, instead.
See Also
Functions
Objects
MATLAB コマンド
次の MATLAB コマンドに対応するリンクがクリックされました。
コマンドを MATLAB コマンド ウィンドウに入力して実行してください。Web ブラウザーは MATLAB コマンドをサポートしていません。
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)