AI-based detection and identification of low-level nuclear waste: a comparative analysis
Article
Duani Rojas, A, Lagos, L, Upadhyay, H et al. (2024). AI-based detection and identification of low-level nuclear waste: a comparative analysis
. NEURAL COMPUTING & APPLICATIONS, 36(33), 21061-21072. 10.1007/s00521-024-10238-7
Duani Rojas, A, Lagos, L, Upadhyay, H et al. (2024). AI-based detection and identification of low-level nuclear waste: a comparative analysis
. NEURAL COMPUTING & APPLICATIONS, 36(33), 21061-21072. 10.1007/s00521-024-10238-7
Ensuring environmental safety and regulatory compliance at Department of Energy (DOE) sites demands an efficient and reliable detection system for low-level nuclear waste (LLW). Unlike existing methods that rely on human effort, this paper explores the integration of computer vision algorithms to automate the identification of such waste across DOE facilities. We evaluate the effectiveness of multiple algorithms in classifying nuclear waste materials and their adaptability to newly emerging LLW. Our research introduces and implements five state-of-the-art computer vision models, each representing a different approach to the problem. Through rigorous experimentation and validation, we evaluate these algorithms based on performance, speed, and adaptability. The results reveal a noteworthy trade-off between detection performance and adaptability. YOLOv7 shows the best performance and requires the highest effort to detect new LLW. Conversely, OWL-ViT has lower performance than YOLOv7 and requires minimal effort to detect new LLW. The inference speed does not strongly correlate with performance or adaptability. These findings offer valuable insights into the strengths and limitations of current computer vision algorithms for LLW detection. Each developed model provides a specialized solution with distinct advantages and disadvantages, empowering DOE stakeholders to select the algorithm that aligns best with their specific needs.
