2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection

• URL: http://arxiv.org/abs/2006.15507v1
• Date: Sun, 28 Jun 2020 04:50:16 GMT
• Title: 2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection
• Authors: Sijia Chen, Yu Wang, Li Huang, Runzhou Ge, Yihan Hu, Zhuangzhuang Ding, Jie Liao
• Abstract summary: This report introduces a state-of-the-art 2D object detection system for autonomous driving scenarios. We integrate both popular two-stage detector and one-stage detector with anchor free fashion to yield a robust detection.
• Score: 7.807118356899879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons. In this report, we introduce a state-of-the-art 2D object detection system for autonomous driving scenarios. Specifically, we integrate both popular two-stage detector and one-stage detector with anchor free fashion to yield a robust detection. Furthermore, we train multiple expert models and design a greedy version of the auto ensemble scheme that automatically merges detections from different models. Notably, our overall detection system achieves 70.28 L2 mAP on the Waymo Open Dataset v1.2, ranking the 2nd place in the 2D detection track of the Waymo Open Dataset Challenges.

Related papers

• Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations [13.529124221397822]
  We introduce a novel introspection solution for 2D object detection based on Deep Neural Networks (DNNs) We implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.
  arXiv  Detail & Related papers  (2024-03-02T10:56:14Z)
• RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving [3.8917150802484994]
  RMMDet is a road-side multitype and multigroup sensor detection system for autonomous driving. We use a ROS-based virtual environment to simulate real-world conditions. We produce local datasets and real sand table field, and conduct various experiments.
  arXiv  Detail & Related papers  (2023-03-09T12:13:39Z)
• 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)
• CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving [117.87070488537334]
  We introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors. The performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR. We experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA.
  arXiv  Detail & Related papers  (2022-03-15T08:32:56Z)
• Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data [19.63193201107591]
  7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users. We develop an approach using a weakly supervised method of fine tuning 3D object detectors for traffic observation cameras. Our method achieves vehicle 7DoF pose prediction accuracy on our dataset comparable to the top performing monocular 3D object detectors on autonomous vehicle datasets.
  arXiv  Detail & Related papers  (2021-10-21T08:26:48Z)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)
• 2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection [26.086623067939605]
  In this report, we introduce a real-time method to detect the 2D objects from images. We leverage accelerationRT to optimize the inference time of our detection pipeline. Our framework achieves the latency of 45.8ms/frame on an Nvidia Tesla V100 GPU.
  arXiv  Detail & Related papers  (2021-06-16T11:32:03Z)
• Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing [42.03797195839054]
  We propose a dual-modality modularized methodology for the robust detection of abnormal vehicles. For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies. Experiments conducted on the Track 4 testset of the NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and 3.4039 root mean square error (RMSE)
  arXiv  Detail & Related papers  (2021-06-09T12:04:25Z)
• Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera [83.31666463259849]
  We propose a method to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. We show that self-supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained using manual annotations. Our method is an effective way to improve person detectors during deployment without any additional labeling effort.
  arXiv  Detail & Related papers  (2020-12-16T12:10:04Z)
• Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal [87.11988786121447]
  This paper presents a framework for 3D object detection and tracking for autonomous vehicles. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
  arXiv  Detail & Related papers  (2020-08-21T20:36:21Z)
• DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data [81.06749792332641]
  We propose a person detection network which uses an alternative strategy to combine scans obtained at different times. DR-SPAAM keeps the intermediate features from the backbone network as a template and recurrently updates the template when a new scan becomes available. On the DROW dataset, our method outperforms the existing state-of-the-art, while being approximately four times faster.
  arXiv  Detail & Related papers  (2020-04-29T11:01:44Z)

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.