diff --git a/model_zoo/official/lite/image_classification/README.en.md b/model_zoo/official/lite/image_classification/README.en.md
index fc3ff983e4..11e05d99b6 100644
--- a/model_zoo/official/lite/image_classification/README.en.md
+++ b/model_zoo/official/lite/image_classification/README.en.md
@@ -20,7 +20,7 @@ The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) an
 
     ![start_sdk](images/sdk_management.png)
 
-    If you have any Android Studio configuration problem when trying this demo, please refer to item 5 to resolve it.
+    If you have any Android Studio configuration problem when trying this demo, please refer to item 4 to resolve it.
 
 2. Connect to an Android device and runs the image classification application.
 
diff --git a/model_zoo/official/lite/image_classification/README.md b/model_zoo/official/lite/image_classification/README.md
index 0952dcd9ff..1aecc92397 100644
--- a/model_zoo/official/lite/image_classification/README.md
+++ b/model_zoo/official/lite/image_classification/README.md
@@ -20,7 +20,7 @@
 
     ![start_sdk](images/sdk_management.png)
 
-    使用过程中若出现Android Studio配置问题，可参考第5项解决。
+    使用过程中若出现Android Studio配置问题，可参考第4项解决。
 
 2. 连接Android设备，运行图像分类应用程序。
 
diff --git a/model_zoo/official/lite/image_segmentation/README.md b/model_zoo/official/lite/image_segmentation/README.md
index 9ff53a4428..d2583649e9 100644
--- a/model_zoo/official/lite/image_segmentation/README.md
+++ b/model_zoo/official/lite/image_segmentation/README.md
@@ -20,7 +20,7 @@
 
     ![start_sdk](images/sdk_management.png)
 
-    使用过程中若出现Android Studio配置问题，可参考第5项解决。
+    使用过程中若出现Android Studio配置问题，可参考第4项解决。
 
 2. 连接Android设备，运行图像分割应用程序。
 
diff --git a/model_zoo/official/lite/object_detection/README.en.md b/model_zoo/official/lite/object_detection/README.en.md
index 79afffb2b1..79238c47ae 100644
--- a/model_zoo/official/lite/object_detection/README.en.md
+++ b/model_zoo/official/lite/object_detection/README.en.md
@@ -20,7 +20,7 @@ The following section describes how to build and execute an on-device object det
 
    ![start_home](images/home.png)
 
-    If you have any Android Studio configuration problem when trying this demo, please refer to item 5 to resolve it.
+    If you have any Android Studio configuration problem when trying this demo, please refer to item 4 to resolve it.
 
 2. Connect to an Android device and runs the object detection application.
 
diff --git a/model_zoo/official/lite/object_detection/README.md b/model_zoo/official/lite/object_detection/README.md
index a22f0a9eb5..a5bd87ce5b 100644
--- a/model_zoo/official/lite/object_detection/README.md
+++ b/model_zoo/official/lite/object_detection/README.md
@@ -19,7 +19,7 @@
 
     ![start_sdk](images/sdk_management.png)
 
-    使用过程中若出现Android Studio配置问题，可参考第5项解决。
+    使用过程中若出现Android Studio配置问题，可参考第4项解决。
 
 2. 连接Android设备，运行目标检测示例应用程序。
 
diff --git a/model_zoo/official/lite/posenet/README.md b/model_zoo/official/lite/posenet/README.md
index 819f5715b9..515f18b3be 100644
--- a/model_zoo/official/lite/posenet/README.md
+++ b/model_zoo/official/lite/posenet/README.md
@@ -19,7 +19,7 @@
 
     ![start_sdk](images/sdk_management.png)
 
-    使用过程中若出现Android Studio配置问题，可参考第5项解决。
+    使用过程中若出现Android Studio配置问题，可参考第4项解决。
 
 2. 连接Android设备，运行骨骼检测示例应用程序。
 
diff --git a/model_zoo/official/lite/scene_detection/README.md b/model_zoo/official/lite/scene_detection/README.md
new file mode 100644
index 0000000000..77f988d583
--- /dev/null
+++ b/model_zoo/official/lite/scene_detection/README.md
@@ -0,0 +1,291 @@
+## MindSpore Lite 端侧场景检测demo（Android）
+
+本示例程序演示了如何在端侧利用MindSpore Lite C++ API（Android JNI）以及MindSpore Lite 场景检测模型完成端侧推理，对设备摄像头捕获的内容进行检测，并在App图像预览界面中显示连续目标检测结果。
+
+### 运行依赖
+
+- Android Studio >= 3.2 (推荐4.0以上版本)
+- NDK 21.3
+- CMake 3.10
+- Android SDK >= 26
+
+### 构建与运行
+
+1. 在Android Studio中加载本示例源码，并安装相应的SDK（指定SDK版本后，由Android Studio自动安装）。
+
+    ![start_home](images/home.png)
+
+    启动Android Studio后，点击`File->Settings->System Settings->Android SDK`，勾选相应的SDK。如下图所示，勾选后，点击`OK`，Android Studio即可自动安装SDK。
+
+    ![start_sdk](images/sdk_management.png)
+
+    使用过程中若出现Android Studio配置问题，可参考下表解决：
+
+    |      | 报错                                                         | 解决方案                                                     |
+    | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+    | 1    | Gradle sync failed: NDK not configured.                      | 在local.properties中指定安装的ndk目录：ndk.dir={ndk的安装目录} |
+    | 2    | Requested NDK version did not match the version requested by ndk.dir | 可手动下载相应的[NDK版本](https://developer.android.com/ndk/downloads?hl=zh-cn)，并在Project Structure - Android NDK location设置中指定SDK的位置（可参考下图完成） |
+    | 3    | This version of Android Studio cannot open this project, please retry with Android Studio or newer. | 在工具栏-help-Checkout for Updates中更新版本                 |
+    | 4    | SSL peer shut down incorrectly                               | 重新构建                                                     |
+
+    ![project_structure](images/project_structure.png)
+
+2. 连接Android设备，运行场景检测示例应用程序。
+
+    通过USB连接Android设备调试，点击`Run 'app'`即可在你的设备上运行本示例项目。
+   > 编译过程中Android Studio会自动下载MindSpore Lite、模型文件等相关依赖项，编译过程需做耐心等待。
+
+    ![run_app](images/run_app.PNG)
+
+    Android Studio连接设备调试操作，可参考<https://developer.android.com/studio/run/device?hl=zh-cn>。
+
+3. 在Android设备上，点击“继续安装”。完成之后即可在手机上体验场景检测功能。
+
+    ![install](images/install.jpg)
+
+## 示例程序详细说明  
+
+端侧场景检测Android示例程序分为JAVA层和JNI层，其中，JAVA层主要通过Android Camera 2 API实现摄像头获取图像帧，以及相应的图像处理（针对推理结果画框）等功能；JNI层在[Runtime](https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/runtime.html)中完成模型推理的过程。
+
+> 此处详细说明示例程序的JNI层实现，JAVA层运用Android Camera 2 API实现开启设备摄像头以及图像帧处理等功能，需读者具备一定的Android开发基础知识。
+
+### 示例程序结构
+
+```text
+app
+|
+├── libs # 存放demo jni层编译出的库文件
+│   └── arm64-v8a
+│       │── libmlkit-label-MS.so #
+|
+├── src/main
+│   ├── assets # 资源文件
+|   |   └── mobilenetv2.ms # 存放模型文件
+│   |
+│   ├── cpp # 模型加载和预测主要逻辑封装类
+|   |   ├── mindspore-lite-x.x.x-mindata-arm64-cpu # minspore源码编译出的调用包,包含demo jni层依赖的库文件及相关的头文件
+|   |   |   └── ...
+│   |   |
+|   |   ├── MindSporeNetnative.cpp # MindSpore调用相关的JNI方法
+│   ├── java # java层应用代码
+│   │   └── com.huawei.himindsporedemo
+│   │       ├── help # 图像处理及MindSpore JNI调用相关实现
+│   │       │   └── ...
+│   │       └── obejctdetect # 开启摄像头及绘制相关实现
+│   │           └── ...
+│   │
+│   ├── res # 存放Android相关的资源文件
+│   └── AndroidManifest.xml # Android配置文件
+│
+├── CMakeLists.txt # cmake编译入口文件
+│
+├── build.gradle # 其他Android配置文件
+├── download.gradle # APP构建时由gradle自动从HuaWei Server下载依赖的库文件及模型文件
+└── ...
+```
+
+### 配置MindSpore Lite依赖项
+
+Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/build.html)生成`mindspore-lite-{version}-minddata-{os}-{device}.tar.gz`库文件包并解压缩（包含`libmindspore-lite.so`库文件和相关头文件），在本例中需使用生成带图像预处理模块的编译命令。
+
+> version：输出件版本号，与所编译的分支代码对应的版本一致。
+>
+> device：当前分为cpu（内置CPU算子）和gpu（内置CPU和GPU算子）。
+>
+> os：输出件应部署的操作系统。
+
+本示例中，build过程由download.gradle文件自动下载MindSpore Lite 版本文件，并放置在`app/src/main/cpp/`目录下。
+
+> 若自动下载失败，请手动下载相关库文件，解压并放在对应位置：
+
+  mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz [下载链接](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.1/lite/android_aarch64/mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz)
+
+在app的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`的编译支持，如下所示：
+
+```text
+android{
+    defaultConfig{
+        externalNativeBuild{
+            cmake{
+                arguments "-DANDROID_STL=c++_shared"
+            }
+        }
+
+        ndk{
+            abiFilters 'arm64-v8a'
+        }
+    }
+}
+```
+
+在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。
+
+```text
+# Set MindSpore Lite Dependencies.
+set(MINDSPORELITE_VERSION  mindspore-lite-1.0.1-runtime-arm64-cpu)
+include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION})
+add_library(mindspore-lite SHARED IMPORTED )
+add_library(minddata-lite SHARED IMPORTED )
+set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION
+        ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so)
+set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION
+        ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so)
+
+# Link target library.
+target_link_libraries(
+    ...
+    mindspore-lite
+    minddata-lite
+    ...
+)
+```
+
+### 下载及部署模型文件
+
+从MindSpore Model Hub中下载模型文件，本示例程序中使用的场景检测模型文件为`mobilenetv2.ms`，同样通过`download.gradle`脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。
+
+> 若下载失败请手动下载模型文件，mobilenetv2.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)。
+
+### 编写端侧推理代码
+
+在JNI层调用MindSpore Lite C++ API实现端测推理。
+
+推理代码流程如下，完整代码请参见`src/cpp/MindSporeNetnative.cpp`。
+
+1. 加载MindSpore Lite模型文件，构建上下文、会话以及用于推理的计算图。  
+
+    - 加载模型文件
+
+        ```cpp
+        jlong bufferLen = env->GetDirectBufferCapacity(model_buffer);
+        if (0 == bufferLen) {
+            MS_PRINT("error, bufferLen is 0!");
+            return (jlong) nullptr;
+        }
+
+        char *modelBuffer = CreateLocalModelBuffer(env, model_buffer);
+        if (modelBuffer == nullptr) {
+            MS_PRINT("modelBuffer create failed!");
+            return (jlong) nullptr;
+        }
+        ```
+
+    - 创建会话
+
+        ```cpp
+        void **labelEnv = new void *;
+        MSNetWork *labelNet = new MSNetWork;
+        *labelEnv = labelNet;
+
+        mindspore::lite::Context *context = new mindspore::lite::Context;
+        context->thread_num_ = num_thread;
+        context->device_list_[0].device_info_.cpu_device_info_.cpu_bind_mode_ = mindspore::lite::NO_BIND;
+        context->device_list_[0].device_info_.cpu_device_info_.enable_float16_ = false;
+        context->device_list_[0].device_type_ = mindspore::lite::DT_CPU;
+
+        labelNet->CreateSessionMS(modelBuffer, bufferLen, context);
+        delete context;
+        ```
+
+    - 加载模型文件并构建用于推理的计算图
+
+        ```cpp
+        void
+        MSNetWork::CreateSessionMS(char *modelBuffer, size_t bufferLen, mindspore::lite::Context *ctx) {
+            session_ = mindspore::session::LiteSession::CreateSession(ctx);
+            if (session_ == nullptr) {
+                MS_PRINT("Create Session failed.");
+                return;
+            }
+
+            // Compile model.
+            model_ = mindspore::lite::Model::Import(modelBuffer, bufferLen);
+            if (model_ == nullptr) {
+                ReleaseNets();
+                MS_PRINT("Import model failed.");
+                return;
+            }
+
+            int ret = session_->CompileGraph(model_);
+            if (ret != mindspore::lite::RET_OK) {
+                ReleaseNets();
+                MS_PRINT("CompileGraph failed.");
+                return;
+            }
+        }
+        ```
+
+2. 将输入图片转换为传入MindSpore模型的Tensor格式。
+
+    ```cpp
+    // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing
+        LiteMat lite_mat_bgr,lite_norm_mat_cut;
+
+       if (!BitmapToLiteMat(env, srcBitmap, lite_mat_bgr)){
+        MS_PRINT("BitmapToLiteMat error");
+           return NULL;
+       }
+       int srcImageWidth = lite_mat_bgr.width_;
+       int srcImageHeight = lite_mat_bgr.height_;
+       if(!PreProcessImageData(lite_mat_bgr, lite_norm_mat_cut)){
+        MS_PRINT("PreProcessImageData error");
+           return NULL;
+       }
+       ImgDims inputDims;
+       inputDims.channel =lite_norm_mat_cut.channel_;
+       inputDims.width = lite_norm_mat_cut.width_;
+       inputDims.height = lite_norm_mat_cut.height_;
+
+       // Get the mindsore inference environment which created in loadModel().
+       void **labelEnv = reinterpret_cast<void **>(netEnv);
+       if (labelEnv == nullptr) {
+           MS_PRINT("MindSpore error, labelEnv is a nullptr.");
+           return NULL;
+       }
+       MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
+
+       auto mSession = labelNet->session;
+       if (mSession == nullptr) {
+           MS_PRINT("MindSpore error, Session is a nullptr.");
+           return NULL;
+       }
+       MS_PRINT("MindSpore get session.");
+
+       auto msInputs = mSession->GetInputs();
+       auto inTensor = msInputs.front();
+
+       float *dataHWC = reinterpret_cast<float *>(lite_norm_mat_cut.data_ptr_);
+       // copy input Tensor
+       memcpy(inTensor->MutableData(), dataHWC,
+              inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
+       delete[] (dataHWC);
+   ```
+
+3. 对输入Tensor按照模型进行推理，获取输出Tensor。
+
+   - 图执行，端测推理。
+
+        ```cpp
+        // After the model and image tensor data is loaded, run inference.
+        auto status = mSession->RunGraph();
+
+        if (status != mindspore::lite::RET_OK) {
+            MS_PRINT("MindSpore run net error.");
+            return NULL;
+        }
+        ```
+
+   - 获取输出数据。
+
+        ```cpp
+        /**
+         * Get the mindspore inference results.
+         * Return the map of output node name and MindSpore Lite MSTensor.
+         */
+        auto names = mSession->GetOutputTensorNames();
+        std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs;
+        for (const auto &name : names) {
+            auto temp_dat = mSession->GetOutputByTensorName(name);
+            msOutputs.insert(std::pair<std::string, mindspore::tensor::MSTensor *>{name, temp_dat});
+        }
+        ```
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new file mode 100644
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diff --git a/model_zoo/official/lite/scene_detection/images/sdk_management.png b/model_zoo/official/lite/scene_detection/images/sdk_management.png
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diff --git a/model_zoo/official/lite/style_transfer/README.md b/model_zoo/official/lite/style_transfer/README.md
index a464e9b92c..25405731ef 100644
--- a/model_zoo/official/lite/style_transfer/README.md
+++ b/model_zoo/official/lite/style_transfer/README.md
@@ -19,7 +19,7 @@
 
     ![start_sdk](images/sdk_management.png)
 
-    使用过程中若出现Android Studio配置问题，可参考第5项解决。
+    使用过程中若出现Android Studio配置问题，可参考第4项解决。
 
 2. 连接Android设备，运行骨应用程序。