YOLOv11: X Major Improvements Over YOLOv8
YOLO is a series of real-time end-to-end object detection system based on deep learning. Unlike previous algorithms that process images step by step, YOLO completes the entire workflow in 1 step—from image analysis to object recognition. With exceptional speed and accuracy, it has become essential for applications like autonomous driving and security surveillance.
YOLOv11: What' s Improved?
Hiwonder just unveiled its new AI-model robotic car, ROSOrin. In a surprising move, it skips the familiar YOLOv8 and adopts YOLOv11 for its core object detection. Why go with YOLOv11? What makes this model truly stand out? Let' s put it to the test and reveal its performance edge.
Set up a test scene on the lab table, placing a plant, a toy car, and a soccer ball to mimic a variety of everyday objects. Then, switch on ROSOrin's built-in YOLOv11 object detection, and observe how it performs.
From the detection results, it' s clear that YOLOv11 accurately locked onto all targets. All 3 objects were not only fully recognized but also outlined with distinct colored bounding boxes, with class labels — “plant,” “car,” and “football”. More importantly, each label has a confidence score above 0.8, showing that the model detects objects with high accuracy, achieving precise localization and classification.
According to official data, YOLOv11's advantage goes beyond accurate detection — its smart design optimizes the network structure, striking a better balance between computational cost and performance.
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In terms of performance metrics, YOLOv11 achieves higher accuracy than YOLOv8 while using fewer parameters and lower FLOPs. What does this mean for the ROSOrin car? It can deliver superior detection with less hardware demand — preserving power consumption and ensuring smooth object recognition on the move.
YOLOv11 integrates seamlessly with the Ultralytics ecosystem. It supports both quick inference with pre-trained weights and custom training tailored to the car' s needs, maintaining efficiency and low memory usage — all without complex setup.
Core Advantages of YOLOv11
• Higher Accuracy: Every model scale surpasses YOLOv8, with improved mAP, enabling the car to recognize objects more precisely.
• Greater Efficiency: Faster inference speed, especially on CPUs, with fewer parameters and lower computation, ensuring smoother performance for the car.
• Better Balance:Achieves an excellent trade-off between speed and accuracy, adapting seamlessly to both slow-and-detailed and fast-and-responsive scenarios.
• Strong Extensibility: Supports a wide range of computer vision tasks, making it easy to add new features.
• Robust Ecosystem: Backed by the active Ultralytics community and continuous updates, allowing developers to debug and deploy models more efficiently.
YOLOv11 × ROSOrin: Hands-On Autonomous Driving in Action
Integrating the YOLOv11 model with the ROSOrin car goes beyond basic object detection. It serves as the core of an interactive, practical autonomous driving application, letting users move past studying the algorithm to experience how YOLO drives the perception system in real scenarios.
Using its trained deep learning models, ROSOrin brings realistic driving scenarios to life: it reads traffic signs in real time, keeps lanes steadily with line-following strategies, and handles turns, stops, and autonomous parking. Every action serves as a practical exercise in the full “Perception–Decision–Control” loop.
Hiwonder provides a full-stack development tutorial that guides you step by step, helping you efficiently bridge the gap from theory to practice and master the core skills of autonomous driving.
