Recent advancements in quality control across various industries have
increasingly utilized the integration of video cameras and image processing for
effective defect detection. A critical barrier to progress is the scarcity of
comprehensive datasets featuring annotated defects, which are essential for
developing and refining automated defect detection models. This systematic
review, spanning from 2015 to 2023, identifies 15 publicly available datasets
and critically examines them to assess their effectiveness and applicability
for benchmarking and model development. Our findings reveal a diverse landscape
of datasets, such as NEU-CLS, NEU-DET, DAGM, KolektorSDD, PCB Defect Dataset,
and the Hollow Cylindrical Defect Detection Dataset, each with unique strengths
and limitations in terms of image quality, defect type representation, and
real-world applicability. The goal of this systematic review is to consolidate
these datasets in a single location, providing researchers who seek such
publicly available resources with a comprehensive reference.

近年来，质量控制领域不断利用视频摄像和图像处理集成来进行有效的缺陷检测。然而，开发和改进自动缺陷检测模型所需的带有注释缺陷的全面数据集稀缺，阻碍了进展。本系统性综述涵盖 2015 年至 2023 年，鉴别 15 个公开可用的数据集并对其进行关键评估，以评估其用于基准测试和模型开发的有效性和适用性。研究结果揭示了各种数据集的多样化情况，如 NEU-CLS，NEU-DET，DAGM，KolektorSDD，PCB Defect Dataset 和空心圆柱缺陷检测数据集，每个数据集在图像质量、缺陷类型表示和实际应用方面都有其独特的优势和局限性。本系统性综述的目标是将这些数据集整合到一个地方，为寻找此类公开可用资源的研究人员提供全面参考。

基于 PRISMA 的公开数据集系统性回顾：用于工业缺陷检测的基准与模型开发

A PRISMA Driven Systematic Review of Publicly Available Datasets for  Benchmark and Model Developments for Industrial Defect Detection

Today, video cameras are deployed in dense for monitoring physical places
e.g., city, industrial, or agricultural sites. In the current systems, each
camera node sends its feed to a cloud server individually. However, this
approach suffers from several hurdles including higher computation cost, large
bandwidth requirement for analyzing the enormous data, and privacy concerns. In
dense deployment, video nodes typically demonstrate a significant
spatio-temporal correlation. To overcome these obstacles in current approaches,
this paper introduces CONVINCE, a new approach to look at the network cameras
as a collective entity that enables collaborative video analytics pipeline
among cameras. CONVINCE aims at 1) reducing the computation cost and bandwidth
requirements by leveraging spatio-temporal correlations among cameras in
eliminating redundant frames intelligently, and ii) improving vision
algorithms' accuracy by enabling collaborative knowledge sharing among relevant
cameras. Our results demonstrate that CONVINCE achieves an object
identification accuracy of $\sim$91\%, by transmitting only about $\sim$25\% of
all the recorded frames.

本文介绍了基于 CONVINCE 的视频监控系统，该系统通过视觉数据间的协同分析来减少计算成本、可视化带宽并提高目标识别的准确性，实现了约 91％的准确性，仅传输了大约 25％的所有记录帧。

在边缘协作的跨相机视频分析

CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge

As handheld video cameras are now commonplace and available in every
smartphone, images and videos can be recorded almost everywhere at anytime.
However, taking a quick shot frequently yields a blurry result due to unwanted
camera shake during recording or moving objects in the scene. Removing these
artifacts from the blurry recordings is a highly ill-posed problem as neither
the sharp image nor the motion blur kernel is known. Propagating information
between multiple consecutive blurry observations can help restore the desired
sharp image or video. Solutions for blind deconvolution based on neural
networks rely on a massive amount of ground-truth data which is hard to
acquire. In this work, we propose an efficient approach to produce a
significant amount of realistic training data and introduce a novel recurrent
network architecture to deblur frames taking temporal information into account,
which can efficiently handle arbitrary spatial and temporal input sizes. We
demonstrate the versatility of our approach in a comprehensive comparison on a
number of challening real-world examples.

本文介绍了一种使用神经网络的实时视频去模糊技术，将多个连续模糊的图像传递信息给递归神经网络来还原所需的清晰图像或视频。