摘要
深度学习技术具有较强的特征提取和语义建模能力,为提高图像边缘检测精度提供了一种新思路和新途径。针对复杂场景中边缘细节表达不充分、语义感知能力有限等问题,本文提出5种基于深度学习的精度提升策略:执行多尺度特征融合,强化边缘表达;引入关键区域注意力机制,优化检测聚焦;利用对抗训练机制,增强模型鲁棒性;利用领域迁移微调,加强对具体场景的适应;构建多模型协同集成,增强检测综合精度。研究结果表明,上述策略可以多层次地增强模型对细节的识别能力与对复杂场景的适应性,从而为智能视觉分析、图像理解等领域提供一种可扩展的技术路径和方法。
Deep learning technology,with its strong feature extraction and semantic modeling capabilities,provides a new approach and new path for improving the accuracy of image edge detection.In response to the problems of insufficient expression of edge details and limited semantic perception in complex scenes,this paper proposes five strategies for improving accuracy based on deep learning.These strategies include performing multi-scale feature fusion to enhance edge expression;It introduces a key area attention mechanism to optimize detection focus;It utilizes adversarial training mechanisms to enhance model robustness;It utilizes domain transfer fine-tuning to enhance adaptation to specific scenes;It constructs a multi-model collaborative integration to enhance overall detection accuracy of detection.The research results show that the above strategies can enhance the model's ability to recognize details and adapt to complex scenes at multiple levels,thereby providing a scalable technical path and methodological reference for fields such as intelligent visual analysis and image understanding.
作者
杨平
YANG Ping(City Institute,Dalian University of Technology,Dalian,Liaoning Province,116000 China)
出处
《科技资讯》
2025年第20期61-63,共3页
Science & Technology Information
关键词
深度学习
图像边缘检测
精度提升
多尺度融合
Deep learning
Image edge detection
Precision improvement
Multi-scale fusion
