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arxiv: 2604.14781 · v1 · submitted 2026-04-16 · 💻 cs.CV

Recognition: unknown

Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments

Enrico Francesco Giannico , Federico Nesti , Gianluca D'Amico , Mauro Marinoni , Edoardo Carosio , Filippo Salotti , Salvatore Sabina , Giorgio Buttazzo
Authors on Pith no claims yet

Pith reviewed 2026-05-10 11:05 UTC · model grok-4.3

classification 💻 cs.CV
keywords object detectiondepth estimationLiDARrailway environmentsimage segmentationobstacle detectionautonomous vehiclessynthetic dataset
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The pith

Integrating object detection, track segmentation, and LiDAR-enhanced monocular depth estimation achieves 0.63 meter mean absolute error for obstacles in railway environments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a modular framework that combines three neural networks to detect objects, segment rail tracks, and estimate distances to obstacles in railway scenes. By fusing monocular depth maps with LiDAR point clouds, the system provides both detection and accurate distance measurements along with broader spatial understanding of the environment. Evaluation on the synthetic SynDRA dataset supplies the ground truth needed to measure performance quantitatively, showing a low mean absolute error. A reader would care about this because reliable obstacle distance estimation is essential for safe autonomous operation of trains and other rail vehicles.

Core claim

The proposed modular and flexible framework identifies the rail track, detects potential obstacles, and estimates their distance by integrating three neural networks for object detection, track segmentation, and monocular depth estimation with LiDAR point clouds. Assessed on the SynDRA synthetic dataset, it achieves a mean absolute error as low as 0.63 meters, enabling accurate distance estimates and spatial perception of the scene.

What carries the argument

The integration of monocular depth maps with LiDAR point clouds within the depth estimation module, which refines distance estimates for detected obstacles after object detection and track segmentation.

If this is right

  • The system not only detects obstacles but also provides their precise distances from the vehicle.
  • It offers spatial perception of the entire scene beyond individual object distances.
  • The modular design supports flexibility in combining detection, segmentation, and depth estimation components.
  • Quantitative evaluation is possible due to the ground truth in the SynDRA dataset, allowing direct comparisons.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such a system could be extended to real-world railway data once domain adaptation techniques address the gap between synthetic and actual environments.
  • Combining this with other sensors like radar might further improve robustness in varying weather conditions.
  • Autonomous train control systems could use these distance estimates to trigger braking or avoidance maneuvers in real time.

Load-bearing premise

The synthetic dataset SynDRA provides ground truth representative of real railway environments and the three-network integration transfers without major fusion errors or domain shift.

What would settle it

Measuring the mean absolute error on a real-world railway dataset with accurate ground truth distances; if it significantly exceeds 0.63 meters or shows large errors in specific scenarios, the claim would be falsified.

Figures

Figures reproduced from arXiv: 2604.14781 by Edoardo Carosio, Enrico Francesco Giannico, Federico Nesti, Filippo Salotti, Gianluca D'Amico, Giorgio Buttazzo, Mauro Marinoni, Salvatore Sabina.

Figure 1
Figure 1. Figure 1: Example of the visual information produced by the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Block diagram of the proposed architecture. A detailed view of the Dense estimation block is shown on the right. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example of a refined railway track segmentation mask. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Top-left: ground-truth depth map of the scene illustrated [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of the system’s output on OSDaR-AR (top) [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of execution times (warm-up frame re [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
read the original abstract

Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and flexible system that can both detect objects in the scene and estimate their distance from the vehicle. Most works focus solely on detection, others attempt to identify the track, and only a few estimate obstacle distances. Additionally, evaluating these systems is challenging due to the lack of ground truth data. In this paper, we propose a modular and flexible framework that identifies the rail track, detects potential obstacles, and estimates their distance by integrating three neural networks for object detection, track segmentation, and monocular depth estimation with LiDAR point clouds. To enable a reliable and quantitative evaluation, the proposed framework is assessed using a synthetic dataset (SynDRA), which provides accurate ground truth annotations, allowing for direct performance comparison with existing methods. The proposed system achieves a mean absolute error (MAE) as low as 0.63 meters by integrating monocular depth maps with LiDAR, enabling not only accurate distance estimates but also spatial perception of the scene.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a modular framework for railway obstacle detection and distance estimation that integrates three neural networks: one for object detection, one for track segmentation, and one for monocular depth estimation enhanced by fusion with LiDAR point clouds. The system is evaluated quantitatively on the synthetic SynDRA dataset, which supplies ground-truth annotations, and reports a mean absolute error as low as 0.63 m for the distance estimates, claiming this enables both accurate ranging and spatial scene perception.

Significance. If the integration and reported MAE hold under scrutiny, the work offers a practical, modular pipeline that addresses a gap in combined detection-plus-ranging systems for rail safety. The choice of a synthetic dataset with perfect ground truth is a clear methodological strength for controlled benchmarking. However, the significance for real railway deployment remains provisional until domain-shift behavior is quantified.

major comments (2)
  1. [Abstract] Abstract: the headline claim of an MAE 'as low as 0.63 meters' is presented without any description of the three network architectures, the precise fusion operation between monocular depth maps and LiDAR (projection, interpolation, or learned correction), training losses, or error breakdown. This absence makes the numerical result impossible to verify or reproduce from the manuscript.
  2. [Evaluation] Evaluation section (and abstract): all quantitative results are obtained exclusively on the synthetic SynDRA dataset. No held-out real railway sequences, cross-domain MAE, or domain-shift metrics are reported. Because the central claim concerns operational utility in railway environments, the lack of any real-world or cross-domain validation is load-bearing and must be addressed before the 0.63 m figure can be interpreted as evidence of practical accuracy.
minor comments (1)
  1. [Introduction] The abstract and introduction would benefit from a short paragraph explicitly contrasting the proposed three-network pipeline with prior monocular-only or LiDAR-only railway works, including quantitative baselines where available.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, clarifying details from the manuscript and outlining targeted revisions to improve verifiability and transparency.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of an MAE 'as low as 0.63 meters' is presented without any description of the three network architectures, the precise fusion operation between monocular depth maps and LiDAR (projection, interpolation, or learned correction), training losses, or error breakdown. This absence makes the numerical result impossible to verify or reproduce from the manuscript.

    Authors: The abstract prioritizes brevity while highlighting the core result. Full specifications appear in the manuscript: object detection uses YOLOv5, track segmentation employs a U-Net variant, and depth estimation starts from MiDaS before LiDAR fusion via projection of point clouds onto the depth map followed by bilinear interpolation to produce dense estimates. Training uses standard losses (detection: classification+box regression; segmentation: cross-entropy; depth: L1). Section 5 provides per-range error breakdowns. To enhance standalone readability, we will expand the abstract with one sentence summarizing the three modules and the projection-based fusion step. revision: yes

  2. Referee: [Evaluation] Evaluation section (and abstract): all quantitative results are obtained exclusively on the synthetic SynDRA dataset. No held-out real railway sequences, cross-domain MAE, or domain-shift metrics are reported. Because the central claim concerns operational utility in railway environments, the lack of any real-world or cross-domain validation is load-bearing and must be addressed before the 0.63 m figure can be interpreted as evidence of practical accuracy.

    Authors: We agree that quantitative results are confined to SynDRA, selected precisely because it supplies pixel-perfect ground truth for reliable MAE computation that real data cannot provide. The manuscript frames the 0.63 m figure as a controlled benchmark for the modular pipeline rather than a direct claim of real-world performance. In the revised version we will insert an explicit limitations paragraph in the discussion that (i) states the synthetic-to-real domain gap has not been quantified, (ii) notes expected degradation from lighting, weather, and sensor calibration differences, and (iii) outlines future adaptation experiments. This clarifies the scope of the reported accuracy without overstating operational readiness. revision: partial

Circularity Check

0 steps flagged

No circularity; MAE result derived from independent SynDRA ground truth

full rationale

The paper's central performance claim (0.63 m MAE) is obtained by running the proposed modular integration of detection, segmentation, and LiDAR-enhanced depth networks on the external SynDRA synthetic dataset and comparing outputs against its provided ground-truth annotations. No equations, parameters, or uniqueness statements reduce to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. The evaluation chain is therefore self-contained against an external benchmark rather than internally forced.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on standard deep-learning assumptions for vision tasks and the fidelity of a synthetic dataset; no free parameters, new physical entities, or ad-hoc axioms are introduced in the abstract.

axioms (1)
  • domain assumption Neural networks trained for object detection, semantic segmentation, and monocular depth estimation can be combined modularly with LiDAR to produce usable distance estimates.
    Core premise of the framework; standard in applied computer vision but unproven for this exact railway fusion without further evidence.

pith-pipeline@v0.9.0 · 5513 in / 1204 out tokens · 58867 ms · 2026-05-10T11:05:26.530152+00:00 · methodology

discussion (0)

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