During January 2026, [Developer Name] developed an automated trash detection feature for the RoBorregos/home2 repository, focusing on integrating a YOLO-E detector into the existing computer vision pipeline. Using Python and ROS, they built a dedicated detection node that identifies and classifies trash in images, supporting waste-management workflows. Their work included normalizing bounding box data, refining detection labels, and improving service interfaces to ensure reliable and accurate outputs. By aligning implementations with related vision and robotics tasks, applying code quality improvements, and coordinating with other teams, [Developer Name] delivered a robust, maintainable solution that enhances the system’s waste detection capabilities.