Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots

• URL: http://arxiv.org/abs/2506.16821v1
• Date: Fri, 20 Jun 2025 08:21:34 GMT
• Title: Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots
• Authors: Can Lin, Daniele Affinita, Marco E. P. Zimmatore, Daniele Nardi, Domenico D. Bloisi, Vincenzo Suriani,
• Abstract summary: We present a self-supervised learning framework for domain-adaptive feature extraction to enhance ball detection performance.<n>The proposed approach leverages a general-purpose pretrained model to generate pseudo-labels, which are then used in a suite of self-supervised pretext tasks.<n> Experimental results demonstrate that the proposed pipeline outperforms baseline models in terms of accuracy, F1 score, and IoU, while also exhibiting faster convergence.
• Score: 2.111102681327218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust and accurate ball detection is a critical component for autonomous humanoid soccer robots, particularly in dynamic and challenging environments such as RoboCup outdoor fields. However, traditional supervised approaches require extensive manual annotation, which is costly and time-intensive. To overcome this problem, we present a self-supervised learning framework for domain-adaptive feature extraction to enhance ball detection performance. The proposed approach leverages a general-purpose pretrained model to generate pseudo-labels, which are then used in a suite of self-supervised pretext tasks -- including colorization, edge detection, and triplet loss -- to learn robust visual features without relying on manual annotations. Additionally, a model-agnostic meta-learning (MAML) strategy is incorporated to ensure rapid adaptation to new deployment scenarios with minimal supervision. A new dataset comprising 10,000 labeled images from outdoor RoboCup SPL matches is introduced, used to validate the method, and made available to the community. Experimental results demonstrate that the proposed pipeline outperforms baseline models in terms of accuracy, F1 score, and IoU, while also exhibiting faster convergence.

Related papers

• Efficient Lines Detection for Robot Soccer [0.0]
  This paper presents a method for detecting soccer field lines using the ELSED algorithm, extended with a classification step that analyzes RGB color transitions to identify lines belonging to the field.<n>Our approach achieves accuracy comparable to a state-of-the-art deep learning model while offering higher processing speed, making it well-suited for real-time applications on low-power robotic platforms.
  arXiv  Detail & Related papers  (2025-07-25T17:54:51Z)
• A Segmented Robot Grasping Perception Neural Network for Edge AI [0.051776141577794685]
  This work implements Heatmap-Guided Grasp Detection on the GAP9 RISC-V System-on-Chip.<n>The model is optimised using hardware-aware techniques, including input dimensionality reduction, model partitioning, and quantisation.<n> Experimental evaluation on the GraspNet-1Billion benchmark validates the feasibility of fully on-chip inference.
  arXiv  Detail & Related papers  (2025-07-18T14:32:45Z)
• Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems [1.1265248232450553]
  This paper proposes a self-supervised contrastive learning approach for generalizable intrusion detection on raw packet sequences.<n>Our framework exhibits better performance in comparison to existing NetFlow self-supervised methods.<n>Our model provides a strong baseline for supervised intrusion detection with limited labeled data.
  arXiv  Detail & Related papers  (2025-05-12T13:42:00Z)
• A Real-time Anomaly Detection Method for Robots based on a Flexible and Sparse Latent Space [2.0186752447895993]
  Deep learning-based models in robotics face challenges due to limited training data and highly noisy signal features.<n>We present Sparse Masked Autoregressive Flow-based Adversarial AutoEncoders model to address these problems.<n>Our model performs inferences within 1 millisecond, ensuring real-time anomaly detection.
  arXiv  Detail & Related papers  (2025-04-15T13:17:14Z)
• ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks [18.488168353080464]
  Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GML) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs)<n>We propose ATOM, a novel real-time MEA detection framework tailored for GNNs.<n>ATOM integrates sequential modeling and reinforcement learning to dynamically detect evolving attack patterns, while leveraging $k$core embedding to capture the structural properties, enhancing detection precision.
  arXiv  Detail & Related papers  (2025-03-20T20:25:32Z)
• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment [59.61554561979589]
  Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios.<n>Existing edge detection methods face challenges: difficulty balancing detection precision with lightweight models, limited adaptability, and insufficient real-world validation.<n>We propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments.
  arXiv  Detail & Related papers  (2024-12-24T07:28:10Z)
• TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies [95.30717188630432]
  We introduce visual trace prompting to facilitate VLA models' spatial-temporal awareness for action prediction.<n>We develop a new TraceVLA model by finetuning OpenVLA on our own collected dataset of 150K robot manipulation trajectories.<n>We present a compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on our dataset.
  arXiv  Detail & Related papers  (2024-12-13T18:40:51Z)
• 3D Multi-Object Tracking with Semi-Supervised GRU-Kalman Filter [6.13623925528906]
  3D Multi-Object Tracking (MOT) is essential for intelligent systems like autonomous driving and robotic sensing. We propose a GRU-based MOT method, which introduces a learnable Kalman filter into the motion module. This approach is able to learn object motion characteristics through data-driven learning, thereby avoiding the need for manual model design and model error.
  arXiv  Detail & Related papers  (2024-11-13T08:34:07Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [58.60915132222421]
  We introduce an approach that is both general and parameter-efficient for face forgery detection. We design a forgery-style mixture formulation that augments the diversity of forgery source domains. We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• ACTRESS: Active Retraining for Semi-supervised Visual Grounding [52.08834188447851]
  A previous study, RefTeacher, makes the first attempt to tackle this task by adopting the teacher-student framework to provide pseudo confidence supervision and attention-based supervision. This approach is incompatible with current state-of-the-art visual grounding models, which follow the Transformer-based pipeline. Our paper proposes the ACTive REtraining approach for Semi-Supervised Visual Grounding, abbreviated as ACTRESS.
  arXiv  Detail & Related papers  (2024-07-03T16:33:31Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video [71.43912903508765]
  MDAL is a dynamic online adaptive learning scheme for instrument segmentation in robot-assisted surgery. It learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. It outperforms other state-of-the-art methods on two datasets.
  arXiv  Detail & Related papers  (2021-03-24T05:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.