.png){kind=icon}
返回
An instance-level data balancing method for object detection via contextual information alignment
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2024.105155.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The imbalance issues in object detection training data, such as in categories, scales, and spatial distribution, result in detection models failing to effectively fit unbalanced data. Our method aims to mitigate the performance disparity caused by data imbalance from the perspective of instance-level augmentation. Firstly, we designed a dynamic data balancing mechanism (DDBM) to develop category expansion rate, scale ratio rules, and spatial distribution indicators to alleviate data imbalance. Then, based on the pixel-level fine-grained context, a finegrained local object instance augmentation (FLOIA) method is designed to selectively copy the object instance according to the background Mosaic degree. In addition, based on the coarse-grained global context and dynamic balancing mechanism, we proposes a coarse-grained global object instance augmentation (CGOIA) method to establish an object-background association, ensure the alignment of the context information of the object instance and alleviate the data imbalance. We train the proposed instance augmentation-treated datasets on various models, resulting in improved balance across different categories and scales. Additionally, visual analysis validates that this approach balances spatial distributions while conforming to contextual information. Furthermore, this method proves advantageous for training with small-sample datasets.
Keyword:
Object detection
Data imbalance
Instance augmentation
Context alignment
Dynamic data balancing mechanism
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
A full data augmentation pipeline for small object detection based on generative adversarial networks基于生成对抗网络的小目标检测全数据增强流水线.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏分享收藏Construction of a feature enhancement network for small object detection用于小目标检测的特征增强网络的构建PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Deep learning-based detection from the perspective of small or tiny objects: A survey从小型或微小物体的角度进行基于深度学习的检测: 一项调查
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容