ShanghaiTech University Knowledge Management System
| An End-to-end Computer Vision System Architecture | |
| 2022-05-27 | |
| 会议录名称 | 2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
 |
| ISSN | 0271-4302 |
| 发表状态 | 已发表 |
| DOI | 10.1109/ISCAS48785.2022.9937670 |
| 摘要 | To overcome the data movement bottleneck, near-sensor and in-sensor computing are becoming more and more popular. However, in the existing near-/in-sensor computing architectures for vision tasks, the effect of the image signal processing (ISP) pipeline, which is of great importance to the final vision performance [1], is always ignored. In this work, we propose a synthesized RAW image-based end-to-end computer vision paradigm, taking the effect of ISP pipeline into account. In the proposed approach, a generative adversarial network (GAN)-based tool is used to convert the fully processed color images to their corresponding RAW Bayer versions, generating the training data for end-to-end vision models. In the inference stage, RAW images from the sensor are directly fed to the end-to-end model, bypassing the entire ISP pipeline. Experimental results show that by training/tuning the CNN models using synthesized RAW images, it is possible to design an end-to-end (from RAW image to vision task) vision system that directly consumes RAW image data from the sensor with negligible vision performance degradation. By skipping the ISP pipeline, an image sensor can be directly integrated with the back-end vision processor without a complex image processor in the middle, making near-/in-sensor computing a practical approach. |
| 关键词 | Near-sensor in-sensor Bayer pattern images image signal processing (ISP) generative adversarial network(GAN) computer vision |
| 会议地点 | Austin, TX, USA |
| 会议日期 | 27 May-1 June 2022 |
| URL | 查看原文 |
| 收录类别 | EI |
| 来源库 | IEEE |
| 引用统计 | 正在获取...
|
| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251919 |
| 专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_娄鑫组 信息科学与技术学院_博士生 |
| 作者单位 | ShanghaiTech University, School of Information Science and Technology, China |
| 第一作者单位 | 信息科学与技术学院 |
| 第一作者的第一单位 | 信息科学与技术学院 |
| 推荐引用方式 GB/T 7714 | Zhang, Ling,Zhou, Wei,Zhang, Xiangyu,et al. An End-to-end Computer Vision System Architecture[C],2022. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
修改评论
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。