.png){kind=icon}
返回
Transformer-CNN for small image object detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2024.117194.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Object recognition in computer vision technology has been a popular research field in recent years. Although the detection success rate of regular objects has achieved impressive results, small object detection (SOD) is still a challenging issue. In the Microsoft Common Objects in Context (MS COCO) public dataset, the detection rate of small objects is typically half that of regular-sized objects. The main reason is that small objects are often affected by multi-layer convolution and pooling, leading to insufficient details to distinguish them from the background or similar objects, resulting in poor recognition rates or even no results. This paper presents a network architecture, Transformer-CNN, that combines a self-attention mechanism-based transformer and a convolutional neural network (CNN) to improve the recognition rate of SOD. It captures global information through a transformer and uses the translation invariance and translation equivalence of CNN to maximize the retention of global and local features while improving the reliability and robustness of SOD. Our experiments show that the proposed model improves the small object recognition rate by 2 similar to 5 % than the general transformer architectures.
Keyword:
Small object detection (SOD)
Transformer
Convolutional neural network (CNN)
Pattern recognition
期刊
S
IF:
2.7
论文数:
2.8K
被引数:
4.2K
机构
引用论文
.png){kind=icon}
Progressive learning with multi-scale attention network for cross-domain vehicle re-identification
PREAI
分享收藏Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers用于制造和维护中的自动视觉检测的深度学习: 对开放获取论文的调查
OAAI
OAAI分享收藏
Nucleation and initial growth of copper electrodeposits under galvanostatic conditions恒电流条件下铜电沉积的成核和初始生长
PREAI
分享收藏 没有更多内容
没有更多内容