Aims & Scope

Computer Vision and Autonomous Systems (CVAS) is a peer-reviewed, international, open-access journal dedicated to advancing the integration of visual perception technologies and autonomous decision-making across various domains. The journal aims to bridge the gap between computer vision research and the development of practical autonomous systems by publishing impactful, application-oriented research. CVAS serves as a dynamic platform for researchers, engineers, system architects, and practitioners to share innovative algorithms, robust architectures, and real-time implementations that empower intelligent machines and autonomous agents. Emphasizing reliability, safety, and scalability, the journal encourages contributions that address both theoretical and applied challenges in perception, navigation, interaction, and automation. CVAS promotes interdisciplinary collaboration to drive forward next-generation systems that operate intelligently and autonomously in complex real-world environments. The journal welcomes original work that contributes to the design, development, and deployment of visual and autonomous technologies across sectors such as robotics, transportation, manufacturing, and surveillance.

Core Topics Include:

Publication Frequency

CVAS publishes issues on a bi-monthly basis, resulting in six regular issues per year. This ensures a steady and timely dissemination of current research in data science and intelligent systems, allowing authors and readers to stay updated on emerging trends and technological breakthroughs.

Ownership

The Computer Vision and Autonomous Systems (CVAS) journal is owned and published by Mc'Xanza Publications, a global publisher committed to advancing academic knowledge through high-quality, open-access platforms for innovation and applied research.

Archiving

All volumes and issues of CVAS are permanently archived and freely accessible through the journal’s official website. This long-term archiving ensures that published content remains available to researchers, educators, and the public, preserving academic contributions for future reference.

Ethics Statement

CVAS upholds the highest standards of publication ethics and integrity. All submitted manuscripts undergo rigorous double-blind peer review to ensure quality, originality, and scientific validity. The journal adheres to strict ethical guidelines concerning authorship, conflicts of interest, and data integrity. Authors must disclose any financial or personal relationships that could bias their work, and editors and reviewers must treat all manuscripts with confidentiality and impartiality. CVAS follows the COPE (Committee on Publication Ethics) principles and takes proactive measures to prevent research misconduct, including falsification, fabrication, and unethical experimentation. The editorial board reserves the right to retract any article found to be in violation of these ethical standards. By promoting transparency, accountability, and academic rigor, CVAS aims to foster a trustworthy and credible environment for the dissemination of impactful research.

Plagiarism Policy

CVAS enforces a zero-tolerance policy on AI-generated plagiarism and maintains a strict upper limit of 10% manual plagiarism as verified through reliable plagiarism detection tools. All submissions are screened at the initial stage to detect duplicate or unethical content. AI-generated manuscripts or those lacking original human intellectual input will be immediately rejected. Authors are encouraged to ensure their work is entirely original and properly cited. Any breach of this policy may lead to manuscript rejection or post-publication retraction. Upholding originality is crucial to maintaining the integrity and academic value of the journal.

Publication Charges

CVAS is committed to supporting open-access academic publishing without placing a financial burden on contributors. We are proud to offer zero publication charges for all authors. This means there are no fees for submission, peer review, or publication, allowing researchers from all regions and institutions to freely disseminate their work. Our aim is to encourage high-quality, accessible research in data science and machine intelligence without financial barriers. Authors can submit and publish their work in CVAS with the confidence that there are no hidden costs or article processing charges (APCs) at any stage of the process.

Copyright Form

All authors must complete and submit a Copyright Transfer Form upon acceptance of their manuscript. This form ensures proper transfer of rights for publication while retaining author attribution.

Download the Copyright Form

Manuscript Template

Authors are encouraged to use the official CVAS manuscript template to prepare their submissions in a consistent and professional format. This template helps streamline the review and production process.

Download the Manuscript Template