Revolutionizing Object Detection with Synthetic Occlusion
In the realm of computer vision, object detection has become a crucial component of various applications, including self-driving cars, surveillance systems, and medical imaging. However, real-world scenarios often involve cluttered and complex environments, making accurate object detection a challenging task. This is where synthetic occlusion comes into play – a powerful technique to enhance the robustness of object detection models.
What is Synthetic Occlusion?
Synthetic occlusion involves intentionally introducing virtual objects in front of other objects within the training dataset, simulating real-world clutter and variability. This can include adding virtual trees, poles, or other obstacles in images of roads, pedestrians, or vehicles. By doing so, the model learns to detect objects even when they are partially or fully occluded, mimicking the complexities of real-world scenarios.
**Benefits of Synthetic Occl...
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