Merge pull request #29220 from omrope79:doc_optimizations_v4

[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

doc/js_tutorials/js_assets/js_object_detection.html

@@ -11,7 +11,7 @@
 <h2>Object Detection Example</h2>
 <p>
     This tutorial shows you how to write an object detection example with OpenCV.js.<br>
-    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    To try the example you should click the <b>modelFile</b> button to upload an ONNX model file.
     You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
     Then You should change the parameters in the first code snippet according to the uploaded model.
     Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
@@ -45,13 +45,6 @@
                 </div>
             </td>
         </tr>
-        <tr>
-            <td>
-                <div class="caption">
-                    configFile <input type="file" id="configFile">
-                </div>
-            </td>
-        </tr>
     </table>
 </div>
 
@@ -84,8 +77,8 @@
 
 <script id="codeSnippet" type="text/code-snippet">
 inputSize = [300, 300];
-mean = [127.5, 127.5, 127.5];
-std = 0.007843;
+mean = [0, 0, 0];
+std = 1;
 swapRB = false;
 confThreshold = 0.5;
 nmsThreshold = 0.4;
@@ -94,14 +87,14 @@ nmsThreshold = 0.4;
 outType = "SSD";
 
 // url for label file, can from local or Internet
-labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_pascal_voc.txt";
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/5.x/samples/data/dnn/object_detection_classes_coco.txt";
 </script>
 
 <script id="codeSnippet1" type="text/code-snippet">
 main = async function() {
     const labels = await loadLables(labelsUrl);
     const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
-    let net = cv.readNet(configPath, modelPath);
+    let net = cv.readNet(modelPath);
     net.setInput(input);
     const start = performance.now();
     const result = net.forward();
@@ -305,7 +298,7 @@ postProcess = function(result, labels) {
     let ctx = canvas.getContext('2d');
     let img = new Image();
     img.crossOrigin = 'anonymous';
-    img.src = 'lena.png';
+    img.src = 'lena.jpg';
     img.onload = function() {
         ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
     };
@@ -330,22 +323,12 @@ postProcess = function(result, labels) {
         loadImageToCanvas(e, 'canvasInput');
     });
 
-    let configPath = "";
-    let configFile = document.getElementById('configFile');
-    configFile.addEventListener('change', async (e) => {
-        initStatus();
-        configPath = await loadModel(e);
-        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
-    });
-
     let modelPath = "";
     let modelFile = document.getElementById('modelFile');
     modelFile.addEventListener('change', async (e) => {
         initStatus();
         modelPath = await loadModel(e);
         document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
-        configPath = "";
-        configFile.value = "";
     });
 
     utils.loadOpenCv(() => {