Object Detection for Autonomous Dozers

• URL: http://arxiv.org/abs/2208.08570v1
• Date: Wed, 17 Aug 2022 23:46:14 GMT
• Title: Object Detection for Autonomous Dozers
• Authors: Chun-Hao Liu and Burhaneddin Yaman
• Abstract summary: We introduce a new type of autonomous vehicle - an autonomous dozer that is expected to complete construction site tasks in an efficient, robust, and safe manner. To better handle the path planning for the dozer and ensure construction site safety, object detection plays one of the most critical components among perception tasks.
• Score: 4.1245904895794085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new type of autonomous vehicle - an autonomous dozer that is expected to complete construction site tasks in an efficient, robust, and safe manner. To better handle the path planning for the dozer and ensure construction site safety, object detection plays one of the most critical components among perception tasks. In this work, we first collect the construction site data by driving around our dozers. Then we analyze the data thoroughly to understand its distribution. Finally, two well-known object detection models are trained, and their performances are benchmarked with a wide range of training strategies and hyperparameters.

Related papers

• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Object criticality for safer navigation [1.565361244756411]
  Given an object detector, filtering objects based on their relevance, reduces the risk of missing relevant objects, decreases the likelihood of dangerous trajectories, and improves the quality of trajectories in general. We show that, given an object detector, filtering objects based on their relevance, reduces the risk of missing relevant objects, decreases the likelihood of dangerous trajectories, and improves the quality of trajectories in general.
  arXiv  Detail & Related papers  (2024-04-25T09:02:22Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset [120.08736654413637]
  The quality of the features extracted by the backbone architecture can have a significant impact on the overall detection performance. Our study evaluates three well-known autonomous vehicle datasets, namely KITTI, NuScenes, and BDD, to compare the performance of different backbone architectures on object detection tasks.
  arXiv  Detail & Related papers  (2023-09-15T17:32:15Z)
• Towards Improving Workers' Safety and Progress Monitoring of Construction Sites Through Construction Site Understanding [0.0]
  We propose a lightweight Optimized Positioning (OP) module to improve channel relation based on global feature affinity association. OP-Net is a general deep neural network module that can be plugged into any deep neural network. A benchmark test using SODA demonstrated that our OP-Net was capable of achieving new state-of-the-art performance in accuracy.
  arXiv  Detail & Related papers  (2022-10-27T20:33:46Z)
• Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring [0.0]
  Multitask Detection, neural Pose and Estimation Network (DSPM) We propose our Multitask Detection, neural Pose and Estimation Network (DSPM) Our architecture allows a flexible combination of the three mentioned tasks during a simple end-to-end training. We perform comprehensive evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the superior performance.
  arXiv  Detail & Related papers  (2022-05-03T14:11:18Z)
• Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures [1.8492669447784602]
  We propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. We apply our model on the recent autonomous driving dataset nuScenes, and we compare nine object detectors. Results show that, in several settings, object detectors that perform best according to the nuScenes ranking are not the preferable ones when the focus is shifted on safety and reliability.
  arXiv  Detail & Related papers  (2022-03-04T09:31:20Z)
• Self-Supervised Object Detection via Generative Image Synthesis [106.65384648377349]
  We present the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world images without bounding box annotations to learn to synthesize and detect objects. Our work advances the field of self-supervised object detection by introducing a successful new paradigm of using controllable GAN-based image synthesis for it.
  arXiv  Detail & Related papers  (2021-10-19T11:04:05Z)
• Landmark Policy Optimization for Object Navigation Task [77.34726150561087]
  This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments. Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal. We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks.
  arXiv  Detail & Related papers  (2021-09-17T12:28:46Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)
• Object-Driven Active Mapping for More Accurate Object Pose Estimation and Robotic Grasping [5.385583891213281]
  The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process. By combining the mapping module and the exploration strategy, an accurate object map that is compatible with robotic grasping can be generated.
  arXiv  Detail & Related papers  (2020-12-03T09:36:55Z)

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.