vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

2026-08-22 00:11:13 +08:00
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+Object Detection using CNNs {#tutorial_dnn_objdetect}
+===========================
+
+# Building
+
+Build samples of "dnn_objectect" module. Refer to OpenCV build tutorials for details.
+Enable `BUILD_EXAMPLES=ON` CMake option and build these targets (Linux):
+- example_dnn_objdetect_image_classification
+- example_dnn_objdetect_obj_detect
+
+Download the weights file and model definition file from `opencv_extra/dnn_objdetect`
+
+
+# Object Detection
+
+```bash
+example_dnn_objdetect_obj_detect  <model-definition-file>  <model-weights-file>  <test-image>
+```
+
+All the following examples were run on a laptop with `Intel(R) Core(TM)2 i3-4005U CPU @ 1.70GHz` (without GPU).
+
+The model is incredibly fast taking just `0.172091` seconds on an average to predict multiple bounding boxes.
+
+```bash
+<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/aeroplane.jpg
+
+Total objects detected: 1 in 0.168792 seconds
+------
+Class: aeroplane
+Probability: 0.845181
+Co-ordinates: 41 116 415 254
+------
+```
+
+![Train_Dets](images/aero_det.jpg)
+
+
+```bash
+<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/bus.jpg
+
+Total objects detected: 1 in 0.201276 seconds
+------
+Class: bus
+Probability: 0.701829
+Co-ordinates: 0 32 415 244
+------
+```
+
+![Train_Dets](images/bus_det.jpg)
+
+```bash
+<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/cat.jpg
+
+Total objects detected: 1 in 0.190335 seconds
+------
+Class: cat
+Probability: 0.703465
+Co-ordinates: 34 0 381 282
+------
+```
+
+![Train_Dets](images/cat_det.jpg)
+
+```bash
+<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/persons_mutli.jpg
+
+Total objects detected: 2 in 0.169152 seconds
+------
+Class: person
+Probability: 0.737349
+Co-ordinates: 160 67 313 363
+------
+Class: person
+Probability: 0.720328
+Co-ordinates: 187 198 222 323
+------
+```
+
+![Train_Dets](images/person_multi_det.jpg)
+
+Go ahead and run the model with other images !
+
+
+## Changing threshold
+
+By default this model thresholds the detections at confidence of `0.53`. While filtering there are number of bounding boxes which are predicted, you can manually control what gets thresholded by passing the value of optional arguement `threshold` like:
+
+```bash
+<bin_path>/example_dnn_objdetect_obj_detect  <model-definition-file>  <model-weights-file>  <test-image> <threshold>
+```
+
+Changing the threshold to say `0.0`, produces the following:
+
+![Train_Dets](images/aero_thresh_det.jpg)
+
+That doesn't seem to be that helpful !
+
+# Image Classification
+
+```bash
+example_dnn_objdetect_image_classification  <model-definition-file>  <model-weights-file>  <test-image>
+```
+
+The size of the model being **4.9MB**, just takes a time of **0.136401** seconds to classify the image.
+
+Running the model on examples produces the following results:
+
+```bash
+<bin_path>/example_dnn_objdetect_image_classification  SqueezeNet_deploy.prototxt  SqueezeNet.caffemodel  tutorials/images/aeroplane.jpg
+Best class Index: 404
+Time taken: 0.137722
+Probability: 77.1757
+```
+
+Looking at [synset_words.txt](https://raw.githubusercontent.com/opencv/opencv/3.4.0/samples/data/dnn/synset_words.txt), the predicted class belongs to `airliner`
+
+
+```bash
+<bin_path>/example_dnn_objdetect_image_classification  SqueezeNet_deploy.prototxt  SqueezeNet.caffemodel  tutorials/images/cat.jpg
+Best class Index: 285
+Time taken: 0.136401
+Probability: 40.7111
+```
+
+This belongs to the class: `Egyptian cat`
+
+```bash
+<bin_path>/example_dnn_objdetect_image_classification  SqueezeNet_deploy.prototxt  SqueezeNet.caffemodel  tutorials/images/space_shuttle.jpg
+Best class Index: 812
+Time taken: 0.137792
+Probability: 15.8467
+```
+
+This belongs to the class: `space shuttle`