提升工业回收场景中噪声图像质量并检测小而密集的重叠物体
Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes
- 基于全连接卷积网络设计轻量级图像增强模型
- 在超10万实例数据上验证，显著提升小目标检测精度
- 适合工业质检场景，尤其对低质量图像处理有实用价值
本文针对工业回收场景中的两大挑战展开研究：小而密集且重叠的物体检测（计算机视觉中的关键难题）以及噪声图像的质量提升。研究聚焦于监督式深度学习方法，基于一个新构建的数据集（包含超过10,000张图像和120,000个实例）进行系统评估。通过分析模型的性能、准确率与计算效率，识别出最可靠的检测系统，并揭示其在工业应用中的具体挑战。此外，本文提出一种基于全连接卷积网络的轻量级图像增强模型，用于改善工业环境中低质量图像的视觉效果。相关代码与数据集已开源：https://github.com/o-messai/SDOOD，https://github.com/o-messai/DDSRNet。
原文摘要 · Abstract (English)
This paper tackles two key challenges: detecting small, dense, and overlapping objects (a major hurdle in computer vision) and improving the quality of noisy images, especially those encountered in industrial environments. [1, 2]. Our focus is on evaluating methods built on supervised deep learning. We perform an analysis of these methods, using a newly developed dataset comprising over 10k images and 120k instances. By evaluating their performance, accuracy, and computational efficiency, we identify the most reliable detection systems and highlight the specific challenges they address in industrial applications. This paper also examines the use of deep learning models to improve image quality in noisy industrial environments. We introduce a lightweight model based on a fully connected convolutional network. Additionally, we suggest potential future directions for further enhancing the effectiveness of the model. The repository of the dataset and proposed model can be found at: https://github.com/o-messai/SDOOD, https://github.com/o-messai/DDSRNet
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。