*6.3. Advanced Image Detection*

In this study, we proposed a novel approach for pre-processing images using advanced parallel *k*-means clustering and then applying image detection using YOLOv4. The *k*-means clustering algorithm was used to divide the images into segments, which were then processed in parallel by multiple processors. The parallel-processing of the image segments resulted in a significant reduction in the overall processing time. The *k*-means algorithm is a popular method for clustering data based on similarity. It groups similar data points together and forms clusters. In the proposed approach, *k*-means was used to divide the images into segments, where each segment represented a cluster of similar pixels. The parallel-processing of these segments was achieved by distributing the segments across multiple processors. This allowed for a more efficient use of resources and resulted in a significant reduction in the overall processing time.

After the image had been segmented, the image detection algorithm YOLOv4 was applied to each segment. YOLOv4 is a state-of-the-art object detection algorithm that has been widely used for image-processing tasks. It can accurately detect and classify objects in an image, making it an ideal choice for this application. The proposed approach provided several advantages over traditional image-processing methods. The use of advanced parallel *k*-means clustering allowed for a more efficient use of resources, resulting in faster processing times. Additionally, the application of YOLOv4 to the image segments improved the accuracy of object detection. Overall, the proposed approach was a powerful tool for image processing on mobile devices.
