.png){kind=icon}
返回
RegionNet: A Multi-Object Counting Method Based on Intelligent Region Partitioning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3494532.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The counting of dense targets plays a crucial role in public safety, intelligent traffic management, and other fields, providing critical data support for multiple industries. However, existing counting methods are mostly suitable for visually specific categories and scenes with uniformly distributed targets. When facing complex scenarios with large fluctuations in target density and high freedom of movement, in reality, the effectiveness of these methods is greatly compromised. To address this issue, this study proposes RegionNet: a multi-object counting model based on intelligent region partitioning. Compared to existing methods, we have drawn inspiration from deep reinforcement learning principles to design a region selection module. This module optimizes the ranges of dense and sparse areas in images effectively and reasonably. Additionally, by introducing a dense region counting module and a sparse region counting module, which utilize density estimation and object detection techniques respectively, the model effectively addresses the counting tasks in different regions. This approach successfully overcomes the issue of reduced accuracy faced by traditional counting methods when dealing with significant variations in target distribution scales, thereby significantly enhancing the counting accuracy across various regions. Experimental results demonstrate that the method proposed in this paper significantly outperforms existing technologies in terms of counting performance on multi-visual category target datasets. It exhibits higher accuracy and exceptional robustness.
Keyword:
Object detection
Deep reinforcement learning
Analytical models
Feature extraction
Estimation
Decision making
Data models
Visualization
Predictive models
Computer vision
Density measurement
Multi-objective counting
deep reinforcement learning
intelligent region division
density estimation
computer vision
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
The impact of temperature dependency of the building insulation thermal conductivity in the Canadian climate加拿大气候对建筑保温导热系数温度依赖性的影响
OAAI
.png){kind=icon}
OAAI分享收藏Psychometrics of the Child PTSD Symptom Scale for DSM-5 for Trauma-Exposed Children and AdolescentsOAAI
OAAI分享收藏分享收藏YOLO-SM: A Lightweight Single-Class Multi-Deformation Object Detection NetworkYolo-sm: 一种轻量级的单类多变形目标检测网络
PREAI
分享收藏分享收藏
查看更多内容