In-lab Image Dataset of Foreign Objects and Anomalies in Iron Ore Conveyor Belts

This dataset contains high-speed recordings and extracted frames depicting iron ore flow on a laboratory-scale conveyor belt system, along with several classes of foreign objects (e.g., wood pieces, plastic fragments, tools) manually introduced to simulate contamination scenarios. The conveyor belt measures 35 cm in width by 1.10 m in length and is powered by an electric motor capable of speeds up to approximately 3 m/s. The overhead camera used is the onboard NVIDIA Jetson TX2 OV5693 sensor, which captured video at 120 fps and a resolution of 1280×720, using a GStreamer pipeline for direct-to-disk recording.
The dataset is organized into multiple folders:
Original-raw-videos: Contains the unedited MP4 files showing both normal iron ore flow and sequences with introduced foreign objects.
Image-files: Includes individual frames extracted from each raw video. Subfolders are named after their corresponding source video.
Image-files-manual-split: Separates frames into two categories: normal (only iron ore) and anomalous (foreign objects).
Yolo-dataset-center: It provides center-cropped frames with YOLO-style labels that focus on the central region of the belt. Organized into train/test/valid splits with respective images and labels.
Split-ds-normal-filtered: Offers a final curated version of the dataset, divided into normal (train/test) and anomalous frames for ease of training anomaly detection models.

Scripts are included to replicate the preprocessing steps (e.g., frame extraction, YOLO-style annotations). The dataset may be used to benchmark computer vision tasks such as object detection and anomaly recognition in industrial contexts or laboratory-scale experiments. All files are unannotated by default except where explicitly labeled for demonstration purposes in the “Yolo-dataset-center” subset and partial labels for anomaly segmentation in “Split-ds-normal-filtered.” In this last folder, we only separate normal samples from anomalous ones.