Developing an effective machine vision system is crucial to successfully deploying robotic inspection in open field conditions and controlled environments

Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.

Sign Language Translation (SLT) is an essential tool for overcoming communication barriers faced by the Deaf and Hard-of-Hearing (DHH) community, enabling equal access to vital information and interactions. This need is especially critical in large-scale, multilingual settings such as Hajj and Umrah, where millions of pilgrims, including DHH individuals, gather annually. Despite its significance, SLT remains challenging due to the multimodal nature of sign languages, which differ significantly from spoken languages. Although earlier surveys have reviewed foundational techniques such as gloss-based translation, attention mechanisms, and early machine learning models, they do not address the transformative impact of vision-language models and multimodal large-language models. This survey fills this gap by systematically reviewing state-of-the-art approaches enabled by Artificial Intelligence in SLT, facilitating seamless interaction between DHH and hearing individuals. We synthesize advancements across three key architectural paradigms: adapter-based language models, hierarchical tokenization frameworks, and vision-language pretraining. We also evaluate modern datasets and emerging evaluation metrics while comparing benchmark scores of the latest gloss-free frameworks. Furthermore, we explore current knowledge-based systems designed to assist DHH individuals, along with their limitations. By integrating these contributions, this survey advances SLT research and provides a roadmap for future innovations in low-resource adaptation, ethical AI development, and global accessibility initiatives.