An Improved R-CNN Based Transmission Line Small Target Defect Identification Method

Abstract

The biotechnology industry relies on robust monitoring and maintenance systems to ensure the reliability and efficiency of critical infrastructure, such as bioreactors, pipelines, and other equipment. Small target defect detection plays a vital role in maintaining operational continuity and preventing costly disruptions. This study proposes an enhanced small target defect detection method for biotechnology equipment using an improved Region-based Convolutional Neural Network (R-CNN) framework. The method addresses challenges posed by complex operating conditions, such as diverse structural designs, high-precision requirements, and environmental interference. The core innovation of the proposed approach lies in constructing an optimized network model based on improved R-CNN technology. This model enhances feature extraction and processing speed, achieving a detection accuracy of over 90% while processing each image in milliseconds. Experimental results demonstrate significant improvements in both identification accuracy and processing efficiency compared to traditional methods. The method provides an intelligent and scalable solution for defect detection, enabling real-time monitoring and decision-making in biotechnology equipment maintenance. By leveraging machine learning techniques, the study explores the application of the improved R-CNN model in ensuring the operational reliability of biotechnology infrastructure. This approach facilitates proactive maintenance strategies, reduces downtime, and enhances the longevity of critical systems. The findings underscore the importance of integrating advanced image recognition technologies into biotechnology monitoring frameworks to meet the growing demands of precision and efficiency. This research contributes to the advancement of intelligent defect detection methodologies tailored to biotechnology applications. The proposed method provides a foundation for further innovation in equipment maintenance, paving the way for smart, efficient, and scalable solutions in the biotechnology sector. Future research may extend these findings by integrating IoT and predictive analytics for enhanced monitoring and maintenance capabilities.