Scaling Up Object Detection: The Power of Multi-Scale Anchors Object detection, the ability of a system to identify and locate specific objects within an image or video, is a cornerstone of modern computer vision. But achieving accurate and robust detection across diverse datasets presents a unique challenge. This is where multi-scale anchors come into play, revolutionizing object detection by addressing the inherent limitations of single-scale anchor boxes. The Anchor Box Dilemma: At the heart of many popular object detection algorithms lies the concept of anchor boxes. These pre-defined boxes act as templates, helping the model predict the location and size of an object within an image. However, a single scale of anchor boxes often falls short when confronted with diverse...
Breaking Free from Boxes: How CenterNet and Multi-Scale Anchors Revolutionize Object Detection For years, the world of object detection relied heavily on anchor boxes. These predefined bounding boxes, scattered across an image at various scales and orientations, served as a starting point for identifying objects. While effective, this approach suffered from several limitations: Sensitivity to Anchor Selection: Finding the optimal set of anchors was a complex and often subjective process. Limited Accuracy: Anchors inherently introduce biases, potentially missing objects that fall outside their predefined shapes or scales. Computational Overhead: The sheer number of anchors used could lead to significant computational costs. Enter CenterNet, a groundbreaking object detection algorithm that throws traditional anchor boxes out the window. Instead of relying on...