Object Detection with Faster R-CNN vs. YOLO: A Comparative Analysis of Speed, Accuracy, and Approach
In this post we are developing an object detector project using Faster R-CNN and YOLO which are two different models with the same objective and compare the models, their output and their approach towards this goal.
The first one is Faster R-CNN which is trained on the COCO dataset. The first step is to load the model and put in the evaluation mode. Then we pass the model and our input image to our main Faster R-CNN function which in summary:
opens the image, converts it to RGB format, transforms it into a PyTorch tensor, and add a batch dimension then runs the image through the model to get predictions and at last extracts the bounding boxes, labels, and scores from the prediction and filters them based on a confidence threshold, converts the image to OpenCV format and then draws bounding boxes and labels for each detected object and returns it as the output.
The second one, You Only Look Once (YOLO) reads our input image using OpenCV, converts it from BGR to RGB color space (as YOLOv5 expects RGB input), and then passes it through the model for inference to get the detections. Then it loops through the detections to extract Bounding box coordinates (x1, y1, x2, y2), Confidence score and Class ID and create a label string and draw the bounding box and put the label text on the image using OpenCV functions and return it as the output.
Now the key differences between the two in different categories:
- Speed: YOLO is generally faster because it makes predictions in a single pass.
- Accuracy: Faster R-CNN is often more accurate, especially for small objects, because it examines proposed regions in detail.
Approach:
Faster R-CNN: "Let's find regions that might have objects, then classify them."
YOLO: "Let's divide the image into a grid and predict objects for each cell simultaneously."
Output:
Faster R-CNN outputs vary based on the number of detected objects.
YOLO outputs a fixed-size tensor regardless of the number of objects.
Flexibility: Faster R-CNN can more easily handle objects of varying sizes in the same image In conclusion, YOLO trades some accuracy for speed by simplifying the detection process into a single stage, while Faster R-CNN prioritizes accuracy through its two-stage approach. Both still perform feature extraction, but YOLO indeed skips the separate region proposal step that Faster R-CNN uses.
Output with YOLO
Output with RCNN
Conclusion
This project highlights the strengths of Faster R-CNN and YOLO for object detection. YOLO offers faster detection with a single-stage approach, ideal for real-time applications, while Faster R-CNN provides higher accuracy, especially for small objects, by using a two-stage process. The choice between these models depends on the balance needed between speed and precision for specific use cases.