Automatic optical inspection (AOI) plays a pivotal role in the manufacturing
process, predominantly leveraging high-resolution imaging instruments for
scanning purposes. It detects anomalies by analyzing image textures or
patterns, making it an essential tool in industrial manufacturing and quality
control. Despite its importance, the deployment of models for AOI often faces
challenges. These include limited sample sizes, which hinder effective feature
learning, variations among source domains, and sensitivities to changes in
lighting and camera positions during imaging. These factors collectively
compromise the accuracy of model predictions. Traditional AOI often fails to
capitalize on the rich mechanism-parameter information from machines or inside
images, including statistical parameters, which typically benefit AOI
classification. To address this, we introduce an external modality-guided data
mining framework, primarily rooted in optical character recognition (OCR), to
extract statistical features from images as a second modality to enhance
performance, termed OANet (Ocr-Aoi-Net). A key aspect of our approach is the
alignment of external modality features, extracted using a single
modality-aware model, with image features encoded by a convolutional neural
network. This synergy enables a more refined fusion of semantic representations
from different modalities. We further introduce feature refinement and a gating
function in our OANet to optimize the combination of these features, enhancing
inference and decision-making capabilities. Experimental outcomes show that our
methodology considerably boosts the recall rate of the defect detection model
and maintains high robustness even in challenging scenarios.

通过基于 OCR 的外部模态引导的数据挖掘框架，我们引入一种名为 OANet（Ocr-Aoi-Net）的方法来提取图像的统计特征作为第二个模态，以提升自动光学检测（AOI）模型的性能，并通过特征的精细融合和优化来提高推理和决策能力，在挑战性场景中明显提高缺陷检测模型的召回率并保持高鲁棒性。

OCR 全能者：将多模态引入基于图像的缺陷检测系统

OCR is All you need: Importing Multi-Modality into Image-based Defect  Detection System

In this paper, we develop a hierarchical aerial computing framework composed
of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute
the fully offloaded tasks of terrestrial mobile users which are connected
through an uplink non-orthogonal multiple access (UL-NOMA). In particular, the
problem is formulated to minimize the AoI of all users with elastic tasks, by
adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which
is restricted by the channel state information (CSI) uncertainty and multiple
resource constraints of UAVs and HAP. In order to solve this non-convex
optimization problem, two methods of multi-agent deep deterministic policy
gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to
design the UAVs trajectory and obtain channel, power, and CPU allocations. It
is shown that task scheduling significantly reduces the average AoI. This
improvement is more pronounced for larger task sizes. On the one hand, it is
shown that power allocation has a marginal effect on the average AoI compared
to using full transmission power for all users. On the other hand, compared
with traditional transmissions (fixed method) simulation result shows that our
scheduling scheme has a lower average AoI.

用空中计算框架由高空平台和无人机组成，解决通过上行非正交多址接入连接的移动用户的任务完全卸载问题，通过调整无人机轨迹和资源分配来最小化用户的数据时延，使用多智能体深确定策略梯度和联邦强化学习两种方法优化求解，显示任务调度显著降低了数据时延。