Two-View Fusion based Convolutional Neural Network for Urban Road Detection

Gu, Shuo; Zhang, Yigong; Yang, Jian; Álvarez, Jose M.; Kong, Hui

doi:10.1109/iros40897.2019.8968054

Cited by 30 publications

(9 citation statements)

References 17 publications

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“…MultiNet [44] StixelNet II [45] RBNet [46] TVFNet [47] LC-CRF [48] LidCamNet [49] RBANet [50] DFM-RTFNet (Ours) 12. An example of the experimental results on the KITTI semantic segmentation dataset.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

Wang

Fan

Sun

et al. 2022

IEEE Trans. Cybern.

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Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CNNs) have been proposed for pixel-wise drivable area and road anomaly detection. In addition, some benchmark datasets, such as KITTI and Cityscapes, have been widely used. However, the existing benchmarks are mostly designed for self-driving cars. There lacks a benchmark for ground mobile robots, such as robotic wheelchairs. Therefore, in this paper, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating the existing state-of-the-art single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features. Furthermore, we propose a novel module, referred to as the dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks to fuse different types of visual features effectively and efficiently. The experimental results show that the transformed disparity image is the most informative visual feature and the proposed DFM-RTFNet outperforms the state-of-the-arts. Additionally, our DFM-RTFNet achieves competitive performance on the KITTI road benchmark. Our benchmark is publicly available at https://sites.google.com/view/gmrb.

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Section: Discussionmentioning

confidence: 99%

Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

Wang

Fan

Sun

et al. 2022

IEEE Trans. Cybern.

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“…Moreover, the CNNs with our SNE embedded generally perform better than Fig. 7: Examples on the KITTI road benchmark, where rows (a)-(f) show the freespace detection results obtained by RBNet [10], TVFNet [17], LC-CRF [16], LidCamNet [5], RBANet [28] and our proposed SNE-RoadSeg, respectively. The true positive, false negative and false positive pixels are shown in green, red and blue, respectively.…”

Section: Performance Evaluation Of Our Sne-roadsegmentioning

confidence: 91%

SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection

Fan¹,

Wang²,

Cai³

et al. 2020

Preprint

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Freespace detection is an essential component of visual perception for self-driving cars. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. Hence, in this paper, we first introduce a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Furthermore, we propose a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surface normal information for accurate freespace detection. For research purposes, we publish a large-scale synthetic freespace detection dataset, named Ready-to-Drive (R2D) road dataset, collected under different illumination and weather conditions. The experimental results demonstrate that our proposed SNE module can benefit all the state-of-the-art CNNs for freespace detection, and our SNE-RoadSeg achieves the best overall performance among different datasets.

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“…The research on multi-sensor fusion systems in AVs for environment perception and object detection is well-established in the literature [ 19 , 21 , 30 , 167 , 176 , 177 , 178 ]. Presently, three primary sensor combinations for obstacle detection are prevalent in the literature, including camera-LiDAR (CL); camera-radar (CR); and camera-LiDAR-radar (CLR) sensor combinations.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

490

133

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With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

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Two-View Fusion based Convolutional Neural Network for Urban Road Detection

Cited by 30 publications

References 17 publications

Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

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