.png){kind=icon}
.png){kind=icon}
Return
A line-segment-based non-maximum suppression method for accurate object detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2022.108885.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Computer vision models are currently making great strides in object detection with the rapid development of deep convolutional detectors. However, generating a large number of anchors is an indispensable step in the object detection models for locating targets, which inevitably leads to redundant detections and low computational efficiency. Detecting contours in an image is a fundamental cognitive ability in human vision system, which offers effective evidences for object detection. This paper proposes a novel and simple method by utilizing the distribution of line segments to facilitate the Non-Maximum Suppression (NMS) for the object detection models. Multiple differentiated metrics are designed for the overlap measure between bounding boxes. As a post -processing technique, the proposed segment-based NMS can be easily applied by various models. Furthermore, the proposed method is verified on multiple benchmarks and extensive experiments have been implemented to illustrate its effectiveness. (C) 2022 Published by Elsevier B.V.
Keywords:
Object detection pipelines
Line -segment -based metrics
Non maximum regression
Post -processing technique
Duplicated detection elimination