High-Throughput Detection of Surface Defects in Biotech Infrastructure Using Frequency Domain Filtering: A Case Study on Rust Identification

Abstract

The reliability and safety of biotechnology infrastructure, such as specialized equipment and pipelines, are critical to maintaining operational excellence. Corrosion, often caused by environmental factors, poses a significant risk to these systems, necessitating efficient and accurate methods for detecting and addressing defects. This study proposes a batch recognition method for large-area surface defects, such as rust, using a frequency domain filtering approach, tailored for biotechnology infrastructure maintenance and monitoring. A comprehensive dataset of surface defect images is constructed, representing corrosion scenarios common to biotech equipment and systems. Large-area defect images are first preprocessed using image equalization transformation to enhance contrast and visibility. Subsequently, the frequency domain filtering method is applied to perform average filtering on the images, enabling precise localization of defect areas. Finally, the images are corrected and enhanced using advanced image processing techniques, and the batch recognition process is completed through a cascade gated cyclic unit framework. Experimental results demonstrate the method's robustness and efficiency. The method achieves a 100% accuracy rate in identifying surface defects and an integrity score of 96.20% for batch recognition. Additionally, the process completes the identification of large-area corrosion defects within 29.06 seconds, highlighting its suitability for high-throughput applications. These results validate the method’s effectiveness in ensuring the reliability of critical biotech infrastructure by enabling timely detection and intervention for surface defects. This study underscores the potential of integrating advanced image processing and batch recognition techniques into biotechnology infrastructure management. The proposed approach offers a scalable and efficient solution for maintaining the integrity of biotech systems, ensuring sustained performance, and reducing downtime associated with equipment failures. It provides a foundation for further innovation in automated defect detection and monitoring in biotechnology and related fields.