Semi-Anchored Detector for One-Stage Object Detection

Abstract

A standard one-stage detector is comprised of two tasks: classification and regression. Anchors of different shapes are introduced for each location in the feature map to mitigate the challenge of regression for multi-scale objects. However, the performance of classification can degrade due to the highly class-imbalanced problem in anchors. Recently, many anchor-free algorithms have been proposed to classify locations directly. The anchor-free strategy benefits the classification task but can lead to sup-optimum for the regression task due to the lack of prior bounding boxes. In this work, we propose a semi-anchored framework. Concretely, we identify positive locations in classification, and associate multiple anchors to the positive locations in regression. With ResNet-101 as the backbone, the proposed semi-anchored detector achieves 43.6% mAP on COCO data set, which demonstrates the state-of-art performance among one-stage detectors.

References in corpus (9)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Distilling the Knowledge in a Neural Network
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
• DSSD : Deconvolutional Single Shot Detector
• CenterNet: Keypoint Triplets for Object Detection
• MetaAnchor: Learning to Detect Objects with Customized Anchors
• Region Proposal by Guided Anchoring
• Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
• Consistent Optimization for Single-Shot Object Detection