Updated Android tutorial for MobileNet-SSD detector

- Refreshed images, links, OpenCV API.
- Added more details to Android Mobilenet sample.
- Moved to new location and re-linked tutorials.

doc/tutorials/introduction/android_binary_package/android_dnn_intro.markdown

@@ -0,0 +1,85 @@
+# How to run deep networks on Android device {#tutorial_android_dnn_intro}
+
+@tableofcontents
+
+@prev_tutorial{tutorial_dev_with_OCV_on_Android}
+@next_tutorial{tutorial_android_ocl_intro}
+
+@see @ref tutorial_table_of_content_dnn
+
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 4.9 |
+
+## Introduction
+In this tutorial you'll know how to run deep learning networks on Android device
+using OpenCV deep learning module.
+Tutorial was written for Android Studio Android Studio 2022.2.1.
+
+## Requirements
+
+- Download and install Android Studio from https://developer.android.com/studio.
+
+- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases
+and unpack it (for example, `opencv-4.X.Y-android-sdk.zip`).
+
+- Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD.
+Configuration file `MobileNetSSD_deploy.prototxt` and model weights `MobileNetSSD_deploy.caffemodel`
+are required.
+
+## Create an empty Android Studio project and add OpenCV dependency
+
+Use @ref tutorial_dev_with_OCV_on_Android tutorial to initialize your project and add OpenCV.
+
+## Make an app
+
+Our sample will takes pictures from a camera, forwards it into a deep network and
+receives a set of rectangles, class identifiers and confidence values in range [0, 1].
+
+- First of all, we need to add a necessary widget which displays processed
+frames. Modify `app/src/main/res/layout/activity_main.xml`:
+@include android/mobilenet-objdetect/res/layout/activity_main.xml
+
+- Modify `/app/src/main/AndroidManifest.xml` to enable full-screen mode, set up
+a correct screen orientation and allow to use a camera.
+@code{.xml}
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android">
+
+    <application
+        android:label="@string/app_name">
+@endcode
+@snippet android/mobilenet-objdetect/gradle/AndroidManifest.xml mobilenet_tutorial
+
+- Replace content of `app/src/main/java/com/example/myapplication/MainActivity.java` and set a custom package name if necessary:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial_package
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial
+
+- Put downloaded `deploy.prototxt` and `mobilenet_iter_73000.caffemodel`
+into `app/src/main/res/raw` folder. OpenCV DNN model is mainly designed to load ML and DNN models
+from file. Modern Android does not allow it without extra permissions, but provides Java API to load
+bytes from resources. The sample uses alternative DNN API that initializes a model from in-memory
+buffer rather than a file. The following function reads model file from resources and converts it to
+`MatOfBytes` (analog of `std::vector<char>` in C++ world) object suitable for OpenCV Java API:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_tutorial_resource
+
+And then the network initialization is done with the following lines:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java init_model_from_memory
+
+See also [Android documentation on resources](https://developer.android.com/guide/topics/resources/providing-resources.html)
+
+- Take a look how DNN model input is prepared and inference result is interpreted:
+
+@snippet android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java mobilenet_handle_frame
+
+`Dnn.blobFromImage` converts camera frame to neural network input tensor. Resize and statistical
+normalization are applied. Each line of network output tensor contains information on one detected
+object in the following order: confidence in range [0, 1], class id, left, top, right, bottom box
+coordinates. All coordinates are in range [0, 1] and should be scaled to image size before rendering.
+
+- Launch an application and make a fun!
+![](images/11_demo.jpg)

doc/tutorials/introduction/android_binary_package/android_ocl_intro.markdown

@@ -1,7 +1,7 @@
 Use OpenCL in Android camera preview based CV application {#tutorial_android_ocl_intro}
 =====================================
 
-@prev_tutorial{tutorial_dev_with_OCV_on_Android}
+@prev_tutorial{tutorial_android_dnn_intro}
 @next_tutorial{tutorial_macos_install}
 
 |    |    |

doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown

@@ -2,7 +2,7 @@ Android Development with OpenCV {#tutorial_dev_with_OCV_on_Android}
 ===============================
 
 @prev_tutorial{tutorial_O4A_SDK}
-@next_tutorial{tutorial_android_ocl_intro}
+@next_tutorial{tutorial_android_dnn_intro}
 
 |    |    |
 | -: | :- |