Docs improvements and redirect fixes (#16287)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/guides/object-blurring.md

@@ -132,8 +132,8 @@ For more detailed applications, check the [advantages of object blurring section
 
 ### Can I use Ultralytics YOLOv8 to blur faces in a video for privacy reasons?
 
-Yes, Ultralytics YOLOv8 can be configured to detect and blur faces in videos to protect privacy. By training or using a pre-trained model to specifically recognize faces, the detection results can be processed with OpenCV to apply a blur effect. Refer to our guide on [object detection with YOLOv8](https://docs.ultralytics.com/models/yolov8) and modify the code to target face detection.
+Yes, Ultralytics YOLOv8 can be configured to detect and blur faces in videos to protect privacy. By training or using a pre-trained model to specifically recognize faces, the detection results can be processed with OpenCV to apply a blur effect. Refer to our guide on [object detection with YOLOv8](https://docs.ultralytics.com/models/yolov8/) and modify the code to target face detection.
 
 ### How does YOLOv8 compare to other object detection models like Faster R-CNN for object blurring?
 
-Ultralytics YOLOv8 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLOv8's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our [YOLOv8 documentation](https://docs.ultralytics.com/models/yolov8).
+Ultralytics YOLOv8 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLOv8's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our [YOLOv8 documentation](https://docs.ultralytics.com/models/yolov8/).

docs/en/guides/object-counting.md

@@ -349,7 +349,7 @@ Ultralytics YOLOv8 provides several advantages over other object detection model
 3. **Ease of Integration:** YOLOv8 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
 4. **Flexibility:** Supports various tasks like object detection, segmentation, and tracking with configurable models to meet specific use-case requirements.
 
-Check out Ultralytics [YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8) for a deeper dive into its features and performance comparisons.
+Check out Ultralytics [YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/) for a deeper dive into its features and performance comparisons.
 
 ### Can I use YOLOv8 for advanced applications like crowd analysis and traffic management?
 

docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md

@@ -121,8 +121,8 @@ Using OpenVINO's high-level performance hints and multi-device modes can help st
 
 Yes, Ultralytics YOLO models are highly versatile and can be integrated with various AI frameworks. Options include:
 
-- **TensorRT:** For NVIDIA GPU optimization, follow the [TensorRT integration guide](https://docs.ultralytics.com/integrations/tensorrt).
-- **CoreML:** For Apple devices, refer to our [CoreML export instructions](https://docs.ultralytics.com/integrations/coreml).
-- **TensorFlow.js:** For web and Node.js apps, see the [TF.js conversion guide](https://docs.ultralytics.com/integrations/tfjs).
+- **TensorRT:** For NVIDIA GPU optimization, follow the [TensorRT integration guide](https://docs.ultralytics.com/integrations/tensorrt/).
+- **CoreML:** For Apple devices, refer to our [CoreML export instructions](https://docs.ultralytics.com/integrations/coreml/).
+- **TensorFlow.js:** For web and Node.js apps, see the [TF.js conversion guide](https://docs.ultralytics.com/integrations/tfjs/).
 
-Explore more integrations on the [Ultralytics Integrations page](https://docs.ultralytics.com/integrations).
+Explore more integrations on the [Ultralytics Integrations page](https://docs.ultralytics.com/integrations/).

docs/en/guides/raspberry-pi.md

@@ -94,7 +94,7 @@ Here we will install Ultralytics package on the Raspberry Pi with optional depen
 
 ## Use NCNN on Raspberry Pi
 
-Out of all the model export formats supported by Ultralytics, [NCNN](https://docs.ultralytics.com/integrations/ncnn) delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture). Therefor our recommendation is to use NCNN with Raspberry Pi.
+Out of all the model export formats supported by Ultralytics, [NCNN](https://docs.ultralytics.com/integrations/ncnn/) delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture). Therefor our recommendation is to use NCNN with Raspberry Pi.
 
 ## Convert Model to NCNN and Run Inference
 
@@ -132,7 +132,7 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
 
 !!! tip
 
-    For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options).
+    For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).
 
 ## Raspberry Pi 5 vs Raspberry Pi 4 YOLOv8 Benchmarks
 

docs/en/guides/security-alarm-system.md

@@ -193,7 +193,7 @@ Running Ultralytics YOLOv8 on a standard setup typically requires around 5GB of
 
 ### What makes Ultralytics YOLOv8 different from other object detection models like Faster R-CNN or SSD?
 
-Ultralytics YOLOv8 provides an edge over models like Faster R-CNN or SSD with its real-time detection capabilities and higher accuracy. Its unique architecture allows it to process images much faster without compromising on precision, making it ideal for time-sensitive applications like security alarm systems. For a comprehensive comparison of object detection models, you can explore our [guide](https://docs.ultralytics.com/models).
+Ultralytics YOLOv8 provides an edge over models like Faster R-CNN or SSD with its real-time detection capabilities and higher accuracy. Its unique architecture allows it to process images much faster without compromising on precision, making it ideal for time-sensitive applications like security alarm systems. For a comprehensive comparison of object detection models, you can explore our [guide](https://docs.ultralytics.com/models/).
 
 ### How can I reduce the frequency of false positives in my security system using Ultralytics YOLOv8?
 

docs/en/guides/streamlit-live-inference.md

@@ -152,7 +152,7 @@ Real-time object detection using Streamlit and Ultralytics YOLOv8 can be applied
 - **Retail**: Customer counting, shelf management, and more.
 - **Wildlife and Agriculture**: Monitoring animals and crop conditions.
 
-For more in-depth use cases and examples, explore [Ultralytics Solutions](https://docs.ultralytics.com/solutions).
+For more in-depth use cases and examples, explore [Ultralytics Solutions](https://docs.ultralytics.com/solutions/).
 
 ### How does Ultralytics YOLOv8 compare to other object detection models like YOLOv5 and RCNNs?
 
@@ -162,4 +162,4 @@ Ultralytics YOLOv8 provides several enhancements over prior models like YOLOv5 a
 - **Ease of Use**: Simplified interfaces and deployment.
 - **Resource Efficiency**: Optimized for better speed with minimal computational requirements.
 
-For a comprehensive comparison, check [Ultralytics YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8) and related blog posts discussing model performance.
+For a comprehensive comparison, check [Ultralytics YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/) and related blog posts discussing model performance.

docs/en/guides/triton-inference-server.md

@@ -147,7 +147,7 @@ By following the above steps, you can deploy and run Ultralytics YOLOv8 models e
 
 ### How do I set up Ultralytics YOLOv8 with NVIDIA Triton Inference Server?
 
-Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
+Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
 
 1. **Export YOLOv8 to ONNX format**:
 
@@ -258,7 +258,7 @@ For an in-depth guide on setting up and running Triton Server with YOLOv8, refer
 
 ### How does Ultralytics YOLOv8 compare to TensorFlow and PyTorch models for deployment?
 
-[Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) offers several unique advantages compared to TensorFlow and PyTorch models for deployment:
+[Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) offers several unique advantages compared to TensorFlow and PyTorch models for deployment:
 
 - **Real-time Performance**: Optimized for real-time object detection tasks, YOLOv8 provides state-of-the-art accuracy and speed, making it ideal for applications requiring live video analytics.
 - **Ease of Use**: YOLOv8 integrates seamlessly with Triton Inference Server and supports diverse export formats (ONNX, TensorRT, CoreML), making it flexible for various deployment scenarios.