A data science team is developing an object detection model for an autonomous vehicle's navigation system. The primary operational requirement is to process video frames in real-time with minimal latency to quickly identify pedestrians and other vehicles. While high accuracy is important, the system's ability to make rapid inferences is the most critical constraint. Which of the following object detection architectures is best suited for this specific use case?
An instance segmentation model, such as Mask R-CNN.
A two-stage detector, such as Faster R-CNN.
A traditional sliding window approach combined with a high-accuracy image classifier.
A one-stage detector, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector).
The correct answer is a one-stage detector, such as YOLO or SSD.
Correct: A one-stage detector, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector) - One-stage detectors treat object detection as a single regression problem, directly predicting bounding boxes and class probabilities from the image in a single pass. This unified architecture allows for significantly faster inference speeds, making them ideal for real-time applications like autonomous vehicles where low latency is the primary concern.
Incorrect: A two-stage detector, such as Faster R-CNN - Two-stage detectors first generate a sparse set of region proposals where objects might be located and then classify each proposal in a second stage. This approach generally yields higher accuracy, particularly for small or overlapping objects, but at the cost of being computationally slower. This makes it less suitable when speed is the most critical factor.
Incorrect: An instance segmentation model, such as Mask R-CNN - Mask R-CNN is an extension of the two-stage Faster R-CNN architecture that adds a parallel branch for predicting pixel-level masks for each object. This provides more detailed output (segmentation) than simple bounding boxes but adds computational overhead, making it slower and less optimal than a dedicated one-stage detector when the primary requirement is minimal latency.
Incorrect: A traditional sliding window approach combined with a high-accuracy image classifier - The sliding window technique is a classic but computationally exhaustive approach. It involves scanning an image with a fixed-size window and running a classifier on each window at multiple scales. This method is extremely inefficient and slow compared to modern deep learning architectures and is not practical for real-time applications.
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Why are one-stage detectors like YOLO and SSD faster than two-stage detectors?
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What makes YOLO particularly well-suited for real-time object detection in autonomous vehicles?