A Systematic review on AI-based object recognition in unfavorable weather condition: Curacy and GDPR compliance - Tshipota, Talifhani Calvin; Du, Chunling; Nawej, Claude Mukatshung; Leholo, Sempe Thom (Edelweiss Applied Science and Technology, 2025)
This comprehensive review of the literature examines the latest advancements, challenges, and possibilities in AI-based object detection systems, particularly in the context of adverse weather conditions and GDPR compliance. The study aims to explore how AI models function under unfavorable conditions while adhering to ethical and legal standards. A total of 19 peer-reviewed publications published since 2020 were identified, filtered, and analyzed from reputable databases using a process aligned with PRISMA 2020 guidelines. The findings highlight significant progress in areas such as domain adaptation, multi-modal sensor fusion, and YOLO-based object detectors, with YOLOv7 demonstrating exceptional performance in fog, rain, and snow. However, high computational costs and a scarcity of real-world datasets continue to pose challenges, leading to performance discrepancies. The review emphasizes the importance of privacy-preserving techniques, including differential privacy, real-time anonymization, and privacy-by-design architectures, as essential components for GDPR compliance. The results suggest that future research should prioritize scalable, real-time, and ethically sound object detection algorithms capable of adapting to changing environmental conditions. Practical implications include enhanced compliance and reliability of AI systems used in intelligent surveillance, autonomous vehicles, and smart city infrastructure. Overall, the report provides researchers and policymakers with a foundational understanding to bridge the gap between technological innovation and legal requirements.