Research and application of image recognition technology based on KNN algorithm

In equipment status recognition, accurate monitoring of manhole cover states is critical for ensuring operational safety. Traditional methods that rely on contact switches to determine the status of manhole covers have inherent limitations, as they cannot directly present the open or closed state of the cover. This study focuses on image recognition technology based on the K-Nearest Neighbors (KNN) algorithm. By constructing a KNN classification model, we perform feature extraction and pattern recognition on image data collected throughout the opening and closing process of manhole covers. Distinctive targets are set at specific locations, and images of the manhole covers are captured accordingly. After applying digital image processing techniques for preprocessing, the images are input into the KNN model for computation. The final determination of whether the manhole cover is properly positioned is made based on the categories of the nearest neighbor samples. Experimental results demonstrate that the KNN-based image recognition technology can rapidly and accurately identify in-place target signals, realizing intuitive observation and real-time monitoring of the manhole cover's opening and closing process. This research effectively enhances the reliability of manhole cover status detection. It advances the intelligence of equipment state recognition, providing a novel technical pathway for the intelligent operation and maintenance of urban infrastructure.