FIN: Fast Inference Network for Map Segmentation

• URL: http://arxiv.org/abs/2510.00651v1
• Date: Wed, 01 Oct 2025 08:29:59 GMT
• Title: FIN: Fast Inference Network for Map Segmentation
• Abstract summary: Multi-sensor fusion in autonomous vehicles is becoming more common to offer a more robust alternative for several perception tasks.<n>Camera-radar fusion offers a cost-effective solution by combining rich semantic information from cameras with accurate distance measurements from radar.<n>This work presents a novel and efficient map segmentation architecture, using cameras and radars, in the acrfullbev space.
• Score: 2.6630680698825633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-sensor fusion in autonomous vehicles is becoming more common to offer a more robust alternative for several perception tasks. This need arises from the unique contribution of each sensor in collecting data: camera-radar fusion offers a cost-effective solution by combining rich semantic information from cameras with accurate distance measurements from radar, without incurring excessive financial costs or overwhelming data processing requirements. Map segmentation is a critical task for enabling effective vehicle behaviour in its environment, yet it continues to face significant challenges in achieving high accuracy and meeting real-time performance requirements. Therefore, this work presents a novel and efficient map segmentation architecture, using cameras and radars, in the \acrfull{bev} space. Our model introduces a real-time map segmentation architecture considering aspects such as high accuracy, per-class balancing, and inference time. To accomplish this, we use an advanced loss set together with a new lightweight head to improve the perception results. Our results show that, with these modifications, our approach achieves results comparable to large models, reaching 53.5 mIoU, while also setting a new benchmark for inference time, improving it by 260\% over the strongest baseline models.

Related papers

• Trajectory-Aware Adaptive Inference in Object Detection Models [0.19845233274307664]
  We build upon an existing object detection framework by incorporating GPS trajectory data into the inference process.<n>We introduce an early-exit mechanism in a YOLOv8-based detector that incorporates motion cues.<n>Results demonstrate that this strategy maintains satisfactory detection performance while significantly reducing inference time and computational cost.
  arXiv  Detail & Related papers  (2026-05-12T16:04:07Z)
• Lightweight Spatiotemporal Highway Lane Detection via 3D-ResNet and PINet with ROI-Aware Attention [0.5156484100374058]
  We propose two models that integrate a 3D-ResNet encoder with a Point Instance Network (PINet) decoder.<n>The architecture has been validated through offline training and real-time inference in the Autonomous Systems Laboratory at City, St George's University of London.
  arXiv  Detail & Related papers  (2026-04-02T15:47:16Z)
• Multi-Representation Adapter with Neural Architecture Search for Efficient Range-Doppler Radar Object Detection [16.02875655103583]
  This paper presents an efficient object detection model for Range-Doppler radar maps.<n>We first represent RD radar maps with multi-representation, i.e., heatmaps and grayscale images, to gather high-level object and fine-grained texture features.<n>We employ a One-Shot Neural Architecture Search method to further improve the model's efficiency while maintaining high performance.
  arXiv  Detail & Related papers  (2025-09-01T09:06:53Z)
• FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation [52.89847760590189]
  3D scene understanding is a critical yet challenging task in autonomous driving.<n>Recent methods leverage the range-view representation to improve processing efficiency.<n>We re-design the workflow for range-view-based LiDAR semantic segmentation.
  arXiv  Detail & Related papers  (2025-02-13T12:39:26Z)
• EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera [6.817117737186402]
  Multisensor fusion is essential for autonomous vehicles to accurately perceive, analyze, and plan their trajectories within complex environments. Current methods for registering LiDAR point clouds with images face significant challenges due to inherent differences and computational overhead. We propose EEPNet, an advanced network that leverages modality maps obtained from point cloud projections to enhance registration accuracy.
  arXiv  Detail & Related papers  (2024-09-28T10:28:28Z)
• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras [14.339217121537537]
  Transportation-dependent systems, such as for navigation and logistics, have great potential to benefit from reliable speed estimation. We provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras. Our framework is capable of handling realistic conditions such as camera movements and different video stream inputs automatically.
  arXiv  Detail & Related papers  (2023-09-25T19:02:40Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Real-time Semantic Segmentation with Fast Attention [94.88466483540692]
  We propose a novel architecture for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism. We show that results on multiple datasets demonstrate superior performance with better accuracy and speed compared to existing approaches.
  arXiv  Detail & Related papers  (2020-07-07T22:37:16Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

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.