InspecSafe-V1

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multiple-modality

industrial-scene

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1501, in _prepare_split_single
                  writer.write(example)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 682, in write
                  self.write_examples_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 655, in write_examples_on_file
                  self._write_batch(batch_examples=batch_examples)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 756, in _write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
                  self.pa_writer = pq.ParquetWriter(
                                   ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
                  self.writer = _parquet.ParquetWriter(
                                ^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1514, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 783, in finalize
                  self.write_examples_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 655, in write_examples_on_file
                  self._write_batch(batch_examples=batch_examples)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 756, in _write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
                  self.pa_writer = pq.ParquetWriter(
                                   ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
                  self.writer = _parquet.ParquetWriter(
                                ^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1345, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1523, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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txt string	json dict	__key__ string	__url__ string
"In the oil and gas chemical scenario, there is smoke and accumulated liquid, and personnel are not (...TRUNCATED)	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/oil_chemical-Level01-Wheeled-002880/oil_chemical-Level01-W(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
"In the oil and gas chemical scenario, the potential hazards in the image are personnel smoking and (...TRUNCATED)	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/oil_chemical-Level01-Wheeled-002708/oil_chemical-Level01-W(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
In the power scenario, there is a foreign object on the ground. The safety level is Grade 2.	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/power-Level02-SuspendedRail-003020/power-Level02-Suspended(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
In the tunnel scene, there is water accumulation on the walls. The safety level is grade three.	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/tunnel-Level03-SuspendedRail-003232/tunnel-Level03-Suspend(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
In the oil and gas chemical scene, there is smoke. The safety level is grade one.	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/oil_chemical-Level01-Wheeled-002840/oil_chemical-Level01-W(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
"In the tunnel scenario, personnel are not wearing gloves and not wearing safety helmets. The safety(...TRUNCATED)	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/tunnel-Level02-SuspendedRail-003149/tunnel-Level02-Suspend(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
"In the tunnel scenario, personnel are not wearing masks, gloves, or safety helmets. The safety leve(...TRUNCATED)	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/tunnel-Level02-SuspendedRail-003194/tunnel-Level02-Suspend(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
"In the coal conveying trestle scenario, personnel are using mobile phones, not wearing masks, and n(...TRUNCATED)	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/coal_conveyor-Level02-SuspendedRail-002645/coal_conveyor-L(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
"In the power scenario, the personnel are not wearing gloves, and there is an open flame in the imag(...TRUNCATED)	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgY(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/power-Level01-SuspendedRail-002965/power-Level01-Suspended(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz
In the power scenario, there is an open flame. Therefore, the safety level is Grade One.	{"flags":{},"imageData":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0(...TRUNCATED)	"DATA_PATH/train/Annotations/Anomaly_data/power-Level01-SuspendedRail-002963/power-Level01-Suspended(...TRUNCATED)	hf://datasets/Jennyyiman/InspecSafe-V1@f025e02b9853bcf6af39e86f583b730df972cbb8/train.tar.gz

End of preview.

InspecSafe-V1

Overview

InspecSafe-V1 is a high-quality, multimodal annotated dataset designed for world model construction and analysis in industrial environments. The data was collected from real-world inspection robots deployed across industrial sites and has been carefully cleaned and standardized for research and applications in predictive world modeling for industrial scenarios.

The dataset covers five representative industrial settings: tunnels, power facilities, sintering equipment, oil/gas/chemical plants, and coal conveyor galleries. It was constructed using data from 41 wheeled or rail-mounted inspection robots operating at 2,239 valid inspection waypoints. Across the dataset, multimodal records may include visible-light video, infrared video, audio, depth or LiDAR point clouds, gas concentration readings, temperature, and humidity. Depending on the inspection robot, sensing configuration, and waypoint conditions, each inspection waypoint is associated with the available subset of these modalities rather than necessarily containing all modality types. The available modality types include:

Visible-light video
Infrared video
Audio
Depth or LiDAR point clouds
Gas concentration readings
Temperature
Humidity

Additionally, pixel-level polygonal segmentation annotations are provided for industrial objects in visible-light images. To support downstream tasks, each sample is also accompanied by a semantic scene description and a corresponding safety-level label based on real inspection protocols.

Dataset Format

The dataset is divided into a training set and a test set, both of which are organized in a structured directory layout with aligned multimodal streams and annotations. An overview of the data structure is shown below:

DATA_PATH
├── train
│   ├── Annotations
│   │   ├── Normal_data
│   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560
│   │   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560-001.jpg
│   │   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560-001.json
│   │   │   │   └── coal_conveyor-Level04-SuspendedRail-000560-001.txt
│   │   │   └── ...
│   │   └── Anomaly_data
│   │       ├── coal_conveyor-Level01-SuspendedRail-002486
│   │       │   ├── coal_conveyor-Level01-SuspendedRail-002486-001.jpg
│   │       │   ├── coal_conveyor-Level01-SuspendedRail-002486-001.json
│   │       │   └── coal_conveyor-Level01-SuspendedRail-002486-001.txt
│   │       └── ...
│   ├── Other_modalities
│   │   ├── coal_conveyor-Level04-SuspendedRail-000560
│   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560-visible.mp4
│   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560-infrared.mp4
│   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560-sensor.txt
│   │   │   ├── coal_conveyor-Level04-SuspendedRail-000560-point_cloud.bag
│   │   │   └── coal_conveyor-Level04-SuspendedRail-000560-audio.wav
│   │   └── ...
│   └── Parameters
│       ├── Hardware
│       ├── Device_A.json
│       ├── Device_B.json
│       └── ...
└── test
    ├── Annotations
    │   ├── Normal_data
    │   │   ├── coal_conveyor-Level04-SuspendedRail-000001
    │   │   │   ├── coal_conveyor-Level04-SuspendedRail-000001-001.jpg
    │   │   │   ├── coal_conveyor-Level04-SuspendedRail-000001-001.json
    │   │   │   └── coal_conveyor-Level04-SuspendedRail-000001-001.txt
    │   │   └── ...
    │   └── Anomaly_data
    │       ├── coal_conveyor-Level01-SuspendedRail-002235
    │       │   ├── coal_conveyor-Level01-SuspendedRail-002235-001.jpg
    │       │   ├── coal_conveyor-Level01-SuspendedRail-002235-001.json
    │       │   └── coal_conveyor-Level01-SuspendedRail-002235-001.txt
    │       └── ...
    ├── Other_modalities
    │   ├── coal_conveyor-Level04-SuspendedRail-000001
    │   │   ├── coal_conveyor-Level04-SuspendedRail-000001-visible.mp4
    │   │   ├── coal_conveyor-Level04-SuspendedRail-000001-infrared.mp4
    │   │   ├── coal_conveyor-Level04-SuspendedRail-000001-sensor.txt
    │   │   ├── coal_conveyor-Level04-SuspendedRail-000001-point_cloud.bag
    │   │   └── coal_conveyor-Level04-SuspendedRail-000001-audio.wav
    │   └── ...
    └── Parameters
        ├── Hardware
        ├── Device_A.json
        ├── Device_B.json
        └── ...

Notes:

Inspection point identifier: Each folder name represents an inspection point, such as coal_conveyor-Level04-SuspendedRail-000560. The same identifier is used in both the Annotations and Other_modalities folders to enable cross-modal correspondence.
Inspection instances: A single inspection point may contain multiple inspection instances. In the Annotations folder, these instances are distinguished by numerical suffixes appended to the filenames, such as -001, -002, and -003.
Annotations:
- .jpg: Visible-light image frame for the corresponding inspection instance.
- .json: Pixel-level polygonal segmentation annotations and related metadata.
- .txt: Human-readable semantic description of the scene.
Other modalities:
- .mp4: Visible-light and infrared videos.
- .txt: Sensor logs, including gas concentration, temperature, and humidity.
- .bag: Point-cloud data in ROS bag format.
- .wav: Audio recordings.
Parameters: The Parameters folder contains hardware specifications, software settings, and calibration-related files used to support multimodal interpretation and fusion.

This structure ensures synchronized access across all modalities and supports both supervised learning and world modeling tasks. Each sample metadata (e.g., robot ID, location, timestamp, safety label) is stored in JSON format. Segmentation masks are provided as PNG images with instance IDs matching the annotation JSON.

The shared inspection point identifier allows the multimodal and sensory records in Other_modalities to be linked to one or more annotated inspection instances in Annotations.

Data Splits

Split	Number of Samples
train	3,763
test	1,250

Note: The dataset does not include a separate validation split; users are encouraged to create one from the training set as needed.

License

This dataset is released under the CC-BY-4.0 License.

Acknowledgements

We thank the multimodal recognition algorithm team who contributed to data collection and annotation. This work was supported by TetraBOT.

For questions or contributions, please open an issue in the repository.

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