Maritime Computer Vision Workshop @ CVPR 2026

Challenges / Thermal Object Detection

Thermal Object Detection Challenge

Quick links: Challenge code & data Submit Leaderboard Source dataset paper Ask for help

Quick Start

1. Download the ready-to-use challenge dataset (thermal infrared images) (link coming soon).
2. Train your detector on the training split and tune it on the validation split.
3. Create a COCO-style JSON with predictions for the test set and upload it to the evaluation server (submission link coming soon).

Overview

Night-time and low-visibility conditions are critical for autonomous and assisted navigation of unmanned surface vehicles (USVs). This challenge focuses on object detection in thermal infrared (IR) imagery, encouraging methods robust to non-daylight environments.

The challenge is based on the Maritime Collision Avoidance Dataset Germany, English Channel, and The Netherlands (Gorczak et al.), recorded in German, British, and Dutch waters (2023–2025) and originally annotated using a COLREG-based taxonomy. For this challenge we use thermal infrared (IR) images only.

Task

Given a thermal infrared (IR) image, predict axis-aligned bounding boxes and a class label for each detection.

The challenge uses two classes:

• Vessel
• Navigational object

Dataset

The challenge dataset is derived from the Maritime Collision Avoidance Dataset Germany, English Channel, and The Netherlands (Gorczak et al.). We use thermal infrared (IR) images only and keep the original train/validation split. The new test split is provided as images only (labels withheld) and is used for ranking.

We provide a ready-to-use challenge dataset that already includes all modifications described below. Participants do not need to retrieve the original dataset and apply preprocessing themselves.

Class mapping

The source dataset follows a COLREG-based taxonomy with five classes. For this challenge we simplify the label space to mitigate class imbalance and ambiguity in thermal IR imagery and enable are robst benchmark:

• Vessel = power-driven vessel + sailboat
• Navigational object = seamark + obstacle
• Dropped: seaplane (extremely rare)

Preprocessing

• Thermal polarity normalization: some IR images in the source dataset use inverted thermal scaling; we convert all images so that hotter regions correspond to higher pixel values.

Annotation counts (after merging)

• Train: vessel = 914, navigational object = 1,139
• Validation: vessel = 222, navigational object = 253

Submission format

Participants will run inference on the test images (labels are not provided) and submit a single COCO-style JSON containing the predicted bounding boxes, class labels, and confidence scores.

The JSON is uploaded to the evaluation server, which computes metrics automatically using the COCO evaluation protocol. A sample submission file will be provided.

Evaluation Metrics

We evaluate predictions using the commonly used COCO object detection protocol and report: AP, AP₅₀, AP₇₅, AR₁, and AR₁₀.

The determining metric for winning will be AP. In case of a draw, AP₅₀ counts.

Baselines

We provide a baseline model (Faster R-CNN with ResNet-50 backbone pretrained on COCO) and starter code to support participation and reproducibility:

Participate

1. Download the challenge dataset (coming soon).
2. Train on the training split and tune hyperparameters on the validation split.
3. Generate predictions for the test split and export them as a COCO-style JSON file.
4. Upload your JSON to the evaluation server (coming soon).

Terms and Conditions

• Submissions must be made before the challenge deadline listed on the participation page.
• The winner is determined by the highest AP score (AP₅₀ in case of tie).
• You may use publicly available data for training, but must disclose it at submission time.

If you have questions, please use the MaCVi Support forum.