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The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations

This study discusses the application of the HSV color segmentation method for color-based object detection in digital images. The data used consist of digital images in JPG, PNG, or WebP format containing various colored objects, including red tomatoes, yellow bananas, green apples, orange oranges, purple akebia, brown sapodilla, and blue blueberries. The research process involves converting images from BGR to HSV, determining HSV ranges for each color, creating masks, performing segmentation, analyzing pixels, detecting contours, and visualizing results using bounding boxes. The results show that the HSV method effectively detects objects, separates them from the background, and provides quantitative information, including pixel count, area percentage, and average HSV values for each color. Red, yellow, green, orange, purple, brown, and blue colors were successfully segmented, displaying clear and accurate objects, both for single and multiple objects, under various sizes and lighting conditions. These findings confirm that the HSV method is a simple, fast, and effective approach for color-based image analysis.