RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving

An, Kang; Chen, Yixin; Wang, Suhong; Xiao, Zhifeng

doi:10.3390/app11125621

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…Although the training speed and detection accuracy could be better than YOLOv7, the small model is suitable for embedding in the mobile end [57], which provides the possibility for cotton field detection and counting. The main idea of the CenterNet algorithm is to treat the target as a critical point when creating the model, i.e., the center point of the target boundary frame [58]. The algorithm finds the central point by evaluating the critical point of the target and makes regression predictions on other attributes of the target.…”

Section: Discussionmentioning

confidence: 99%

Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods

Feng

Chen

et al. 2023

Remote Sensing

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Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton’s late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, and deep learning methods are widely used to extract target objects from the complex background. Therefore, this study takes seedling cotton as the research object and uses three deep learning algorithms, YOLOv5, YOLOv7, and CenterNet, for cotton seedling detection and counting using images at six different times of the cotton seedling period based on multispectral images collected by UAVs to develop a model applicable to the whole cotton seedling period. The results showed that when tested with data collected at different times, YOLOv7 performed better overall in detection and counting, and the T4 dataset performed better in each test set. Precision, Recall, and F1-Score values with the best test results were 96.9%, 96.6%, and 96.7%, respectively, and the R2, RMSE, and RRMSE indexes were 0.94, 3.83, and 2.72%, respectively. In conclusion, the UAV multispectral images acquired about 23 days after cotton sowing (T4) with the YOLOv7 algorithm achieved rapid and accurate seedling detection and counting throughout the cotton seedling stage.

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Section: Discussionmentioning

confidence: 99%

Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods

Feng

Chen

et al. 2023

Remote Sensing

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“…e CenterNet network is one of the object detection methods of deep learning, which is widely used in industry [36], transportation [37], and other fields. e principle [33] is modeling the object to be detected as a single point, namely the center point of the bounding box, and then determining the center point through the keypoint heatmap.…”

Section: Technical Principlementioning

confidence: 99%

Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR

Wang

Liu

et al. 2022

Journal of Spectroscopy

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Long-term industrial activities tend to cause surface subsidence and damage to ground facilities and local ecological environment. Monitoring and analyzing surface subsidence is of great significance to prevent potential disasters. The surface type of the Yellow River Delta in China is complex and there are many industrial activities, so it is necessary to monitor the surface subsidence in this area. Small Baseline Subset InSAR (SBAS-InSAR) can monitor the surface subsidence with millimeter-level accuracy, but it takes a long time to process wide images (Sentinel-1) and is seriously affected by atmospheric errors. To avoid these limitations, we constructed a method combining the CenterNet network and SBAS-InSAR (CNSBAS-InSAR). Firstly, the CenterNet network is used to automatically detect the subsidence areas from the wide differential interferogram formed by two SAR satellite images and determine the location of the subsidence area. Then, the SBAS-InSAR monitoring is performed on the detected multiple subsidence areas. Finally, the small-scale subsidence results are obtained. In this study, based on 24 Sentinel-1A satellite images acquired from 10 January 2018 to 24 December 2018, nine subsidence areas in Yellow River Delta were detected. Three of them had long-term surface subsidence. They were located in Zhanhua District, Xianhe Town, and Hongguang Village, respectively. This paper focuses on analyzing these three areas. The maximum subsidence rate of Zhanhua District, Xianhe Town, and Hongguang Village were −135.21 mm/a, −330.91 mm/a, and −209.68 mm/a, respectively. In addition, the analysis showed that precipitation in the Zhanhua District could effectively slow down the subsidence rate of the area. The subsidence of Xianhe Town threatened the safety of the Shugang Expressway. The subsidence of Hongguang Village caused the safety risks of buildings. The results of this study prove that CNSBAS-InSAR method is reliable for monitoring subsidence areas and it can provide a reference for local construction and protection of Yellow River Delta.

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Predicting Vehicle Pose in Six Degrees of Freedom from Single Image in Real-World Traffic Environments Using Deep Pretrained Convolutional Networks and Modified Centernet

Kolekar,

Gite,

Pradhan

et al. 2024

International Journal on Smart Sensing and Intelligent Systems

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The study focuses on intelligent driving, emphasizing the importance of recognizing nearby vehicles and estimating their positions using visual input from a single image. It employs transfer learning techniques, integrating deep convolutional networks’ features into a modified CenterNet model for six-degrees-of-freedom (6DoF) vehicle position estimation. To address the vanishing gradient problem, the model incorporates simultaneous double convolutional blocks with skip connections. Utilizing the ApolloCar3D dataset, which surpasses KITTI in comprehensiveness, the study evaluates pretrained models’ performance using mean average precision (mAP). The recommended model, Center-DenseNet201, achieves a mAP of 11.82% for relative translation thresholds (A3DP-Rel) and 39.92% for absolute translation thresholds (A3DP-Abs). These findings highlight the effectiveness of pretrained models in the modified architecture, enhancing vehicle posture prediction accuracy from single images. The research contributes to autonomous vehicle development, fostering safer and more efficient navigation systems in real-world traffic scenarios.

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RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving

Cited by 3 publications

References 58 publications

Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods

Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods

Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR

Predicting Vehicle Pose in Six Degrees of Freedom from Single Image in Real-World Traffic Environments Using Deep Pretrained Convolutional Networks and Modified Centernet

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