YOLOv12 Real Time Object Detection: What's New?

Is YOLOv12 truly better at object detection?

This is the question on the minds of AI researchers, developers, and industry professionals as real-time object detection becomes more critical in fields like autonomous vehicles, healthcare, and surveillance.

The YOLO (You Only Look Once) series has long been used in the computer vision space, known for its ability to predict bounding boxes and class probabilities in a single pass through the network, delivering both speed and accuracy.

But why does this matter?

In real-time surveillance, even a small delay can mean missing a threat. Faster and more accurate object detection is crucial for monitoring busy areas, securing buildings, and quickly responding to security issues.

For example, spotting suspicious behavior or detecting an intruder just a second earlier could prevent a major security breach.

In high-stakes situations, this improved speed and accuracy could make all the difference in keeping people safe.

What is object detection?

Object detection locates and classifies objects within an image or video. First, a model analyzes the input, identifies potential object locations, and draws bounding boxes around them.

Then, the model assigns a label to each detected object, effectively categorizing it. This process enables machines to "see" and understand visual information, allowing them to perform tasks like autonomous driving, surveillance, and image retrieval.

How object detection used to work?

Earlier YOLO models relied on grid-based detection, dividing images into cells to predict bounding boxes and class probabilities. 

YOLOv11 improved this by improving backbone and neck architecture, which significantly boosts feature extraction capabilities for more precise object detection and complex task performance. 

This architecture includes innovative blocks such as C3k2, SPPF, and C2PSA, which contribute to its enhanced feature extraction and processing efficiency.

YOLOv11 has improved performance on the COCO dataset compared to its predecessors. It achieves a higher mean Average Precision (mAP) score while using 22% fewer parameters than YOLOv8m. 

What new in YOLOv12 ?

YOLOv12 is the newest version of the YOLO object detection system, and it's a big improvement. It solves problems from older versions, like struggling with small objects and being slow. It does this with two main new features:

• Area Attention Mechanism: This helps the model focus on important parts of the image.  
• R-ELAN Architecture: This makes the model more efficient and accurate.

Essentially, YOLOv12 is faster and better at finding objects, especially small ones, making it a major step forward in computer vision.

Area Attention Mechanism: How it works?

YOLOv12's Area Attention Mechanism speeds up object detection by:

1. Divides Feature Maps: The mechanism divides the feature map into smaller segments or areas, allowing it to process each area independently.
2. Reduces Computational Cost: By focusing on smaller areas, it significantly reduces the computational complexity compared to traditional attention mechanisms.
3. Maintains Large Receptive Field: Despite processing in segments, it maintains a large receptive field, ensuring that the model captures a broad context.

Essentially, it's a smarter way to focus on the important parts of an image, making object detection quicker and more efficient.

R-ELAN: Enhanced Feature Learning Through Residual Connection

YOLOv12 also uses R-ELAN, which improves how the model understands images. It does this by:  

1. Improves Feature Integration: R-ELAN actively combines features from various layers, ensuring that the model captures a wide range of contextual information.
2. Alleviates Gradient Bottlenecks: It helps prevent gradient bottlenecks during training, allowing the model to learn more effectively and maintain stability.
3. Enhances Feature Fusion: R-ELAN optimizes how features are merged, leading to better object detection accuracy by ensuring that all relevant information is utilized.  

This means YOLOv12 can better understand images, especially when there are objects of different sizes.

Comparison with YOLOv11

We are using real-time surveillance as an example for detecting object present in the scene, YOLOv11 and YOLOv12 actively compete, each offering unique strengths in accuracy, speed, and efficiency that provide diverse surveillance requirements.

Here, is result of comparison between both model in real time surveillance.

1. YOLOv12 is better at differentiating between different objects due to its attention mechanism

2. YOLOv12 is better at detecting multiple objects due to its R-ELAN architecture but only where the density of objects are higher. It still misses the edge part where density of objects are lower.

3. YOLOv11 beats YOLOv12 in detecting person as it was able to detect them in difficult spot.

How to use YOLOv12

First install ultralytics module, in your system

!pip install ultralytics

using following command line, you can install ultralytics module in system.

Then following code will be use to inference on any sample image

from ultralytics import YOLO

# Load a COCO-pretrained YOLO12n model
model = YOLO("yolo12n.pt")

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

# Run inference with the YOLO12n model on the 'sample.jpg' image
results = model("path/to/sample.jpg")

Conclusion

Both YOLOv11 and YOLOv12 are powerful tools for real-time surveillance, with YOLOv11 excelling in speed and YOLOv12 offering enhanced accuracy and adaptability.

The choice between them depends on specific application requirements, such as the need for ultra-fast inference or superior detection precision.

FAQ

Q1: What are the common algorithms used for object detection?

A: Popular object detection algorithms include YOLO (You Only Look Once), Faster R-CNN, SSD (Single Shot MultiBox Detector), and RetinaNet.

Q2: How does object detection differ from image classification?

A: Image classification assigns a label to an entire image, while object detection identifies and locates multiple objects within an image.

Q3: What are the challenges in object detection?

A: Challenges include variations in object size, lighting, occlusion, background clutter, and fast object movement.

Q4: Can object detection work in real time?

A: Yes, models like YOLO and SSD are optimized for real-time object detection with high frame rates.

References

1. Yolov12 Github(Link)
2. Yolov12 Blog(Link)
3. Yolov12 Paper(Link)