TL;DR
FoveaBox introduces an anchor-free object detection framework that directly predicts object existence and bounding boxes, achieving state-of-the-art results while simplifying the detection process and reducing hyper-parameter sensitivity.
Contribution
It presents a novel anchor-free detection method that predicts semantic maps and bounding boxes without predefined anchors, improving accuracy and generalization.
Findings
Achieves state-of-the-art performance on COCO and Pascal VOC benchmarks.
Eliminates the need for anchor-related hyper-parameters.
Demonstrates robustness and simplicity in object detection.
Abstract
We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its…
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Taxonomy
MethodsAverage Pooling · ResNeXt Block · Step Decay · Random Horizontal Flip · Weight Decay · SGD with Momentum · Non Maximum Suppression · FoveaBox · Grouped Convolution · Bottleneck Residual Block
