Photovoltaic (PV) systems allow us to tap into all abundant solar energy,
however they require regular maintenance for high efficiency and to prevent
degradation. Traditional manual health check, using Electroluminescence (EL)
imaging, is expensive and logistically challenging making automated defect
detection essential. Current automation approaches require extensive manual
expert labeling, which is time-consuming, expensive, and prone to errors. We
propose PV-S3 (Photovoltaic-Semi Supervised Segmentation), a Semi-Supervised
Learning approach for semantic segmentation of defects in EL images that
reduces reliance on extensive labeling. PV-S3 is a Deep learning model trained
using a few labeled images along with numerous unlabeled images. We introduce a
novel Semi Cross-Entropy loss function to train PV-S3 which addresses the
challenges specific to automated PV defect detection, such as diverse defect
types and class imbalance. We evaluate PV-S3 on multiple datasets and
demonstrate its effectiveness and adaptability. With merely 20% labeled
samples, we achieve an absolute improvement of 9.7% in IoU, 29.9% in Precision,
12.75% in Recall, and 20.42% in F1-Score over prior state-of-the-art supervised
method (which uses 100% labeled samples) on UCF-EL dataset (largest dataset
available for semantic segmentation of EL images) showing improvement in
performance while reducing the annotation costs by 80%.

提出了一种用于光致发光图像的缺陷语义分割的 PV-S3（光伏半监督分割）半监督学习方法，通过使用少量标记图像和大量未标记图像训练深度学习模型 PV-S3，并引入一种新的半交叉熵损失函数，实现了在降低注释成本的同时提高了性能。

PV-S3：利用半监督语义分割技术提升光致发光图像自动光伏缺陷检测

PV-S3: Advancing Automatic Photovoltaic Defect Detection using  Semi-Supervised Semantic Segmentation of Electroluminescence Images

In the food industry, assessing the quality of poultry carcasses during
processing is a crucial step. This study proposes an effective approach for
automating the assessment of carcass quality without requiring skilled labor or
inspector involvement. The proposed system is based on machine learning (ML)
and computer vision (CV) techniques, enabling automated defect detection and
carcass quality assessment. To this end, an end-to-end framework called
CarcassFormer is introduced. It is built upon a Transformer-based architecture
designed to effectively extract visual representations while simultaneously
detecting, segmenting, and classifying poultry carcass defects. Our proposed
framework is capable of analyzing imperfections resulting from production and
transport welfare issues, as well as processing plant stunner, scalder, picker,
and other equipment malfunctions. To benchmark the framework, a dataset of
7,321 images was initially acquired, which contained both single and multiple
carcasses per image. In this study, the performance of the CarcassFormer system
is compared with other state-of-the-art (SOTA) approaches for both
classification, detection, and segmentation tasks. Through extensive
quantitative experiments, our framework consistently outperforms existing
methods, demonstrating remarkable improvements across various evaluation
metrics such as AP, AP@50, and AP@75. Furthermore, the qualitative results
highlight the strengths of CarcassFormer in capturing fine details, including
feathers, and accurately localizing and segmenting carcasses with high
precision. To facilitate further research and collaboration, the pre-trained
model and source code of CarcassFormer is available for research purposes at:
https://github.com/UARK-AICV/CarcassFormer.

该研究提出了一种用于自动评估家禽胸脯质量的有效方法，该方法基于机器学习和计算机视觉技术，实现了缺陷检测和胸脯质量评估的自动化。通过使用一种基于 Transformer 的框架，该方法能够从图像中有效地提取视觉表示，并同时检测、分割和分类家禽胸脯的缺陷。经过大量实验比较，该方法在分类、检测和分割任务上优于其他现有方法，并在各种评估指标（如 AP、AP@50 和 AP@75）上展现出显著的改进。

CarcassFormer: 一种基于 Transformer 的家禽屠体缺陷的同时定位、分割和分类的端到端框架

CarcassFormer: An End-to-end Transformer-based Framework for  Simultaneous Localization, Segmentation and Classification of Poultry Carcass  Defect

This paper introduces a robust approach for automated defect detection in
tire X-ray images by harnessing traditional feature extraction methods such as
Local Binary Pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) features,
as well as Fourier and Wavelet-based features, complemented by advanced machine
learning techniques. Recognizing the challenges inherent in the complex
patterns and textures of tire X-ray images, the study emphasizes the
significance of feature engineering to enhance the performance of defect
detection systems. By meticulously integrating combinations of these features
with a Random Forest (RF) classifier and comparing them against advanced models
like YOLOv8, the research not only benchmarks the performance of traditional
features in defect detection but also explores the synergy between classical
and modern approaches. The experimental results demonstrate that these
traditional features, when fine-tuned and combined with machine learning
models, can significantly improve the accuracy and reliability of tire defect
detection, aiming to set a new standard in automated quality assurance in tire
manufacturing.

该研究介绍了一种强大的方法，通过利用传统的特征提取方法如局部二进制模式（LBP）和灰度共生矩阵（GLCM）特征，以及傅里叶和小波特征，结合先进的机器学习技术，在轮胎 X 射线图像中实现自动缺陷检测。实验证明，通过精细调整和结合机器学习模型，这些传统特征可以显著提高轮胎缺陷检测的准确性和可靠性，旨在为轮胎制造业的自动化质量保证建立新的标准。