Intelligent Assisted Driving System Based on Multi-MCU

Xia, Shixu; Xiong, Yuhang; Gao, Hao; Li, Jiashun

doi:10.1088/1742-6596/1676/1/012186

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scale processing module is opened, and the current predicted target position is used as the center position to obtain the dynamic target scale. The optimal target scale is obtained according to the scale evaluation principle (11).…”

Section: 3scale Adaptivementioning

confidence: 99%

“…The outer-loop PID controls the distance between the UAV and the target, using the deviation of the UAV position from the moving target position as an input to the controller and its output as an output quantity to control the flight of the UAV. The real-time 3D coordinate position of the moving target in the world coordinate system can be obtained by coordinate conversion [11]. The actual UAV coordinates are obtained by fusing GPS, inertial odometry, barometer, etc.…”

Section: Dynamic Landing Pid Control Schemementioning

confidence: 99%

“…Miao Wennan et al [10] proposed a tracking algorithm based on target color and target shape for target tracking of mobile robots, and the experiments showed that it has high tracking accuracy and success rate. Xia Lu et al [11] proposed a multi-scale target vision intelligent assisted landing system for the interference in the process of UAV autonomous landing, which improved the landing accuracy of UAV with an average accuracy of 3cm/2°. Liu Yupan [12] addressed the target tracking problem in the UAV autonomous landing process and improved the KCF kernel correlation filtering algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous landing algorithm of UAV based on Target tracking

Wan

Shi²

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The target tracking technology in the UAV autonomous landing process is studied. Based on the analysis of the occlusion, background change, and variable scale problems in the target tracking process, a scale adaptive method for UAV target tracking based on KCF (Kernelized Correlation Filter) algorithm is proposed. The original KCF target tracking framework is adopted to achieve the target position prediction function. Secondly, to address the variable scale problem of moving targets and the target loss problem caused by scale changes, the scale filter of the DSST target tracking algorithm is used. The algorithm is for estimating the scale of moving targets and generating a small-scale filter estimate to improve the tracking efficiency of the UAV for moving targets and reduce the target loss rate. The final test on the OTB dataset showed a 4% improvement in variable scale accuracy and a 2.9% improvement in variable scale success rate. A Gazebo 3D environment scene was set up under the Linux system, and UAV autonomous landing simulation experiments were conducted to verify the improved algorithm’s real-time effectiveness in the UAV autonomous landing application. The simulation results show that the improved KCF target tracking algorithm has real-time performance in the UAV autonomous landing trajectory tracking process.

show abstract

Section: 3scale Adaptivementioning

confidence: 99%

Section: Dynamic Landing Pid Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous landing algorithm of UAV based on Target tracking

Wan

Shi²

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…With the rapid development of science and technology, intelligent driving technology has gradually become a research hotspot in the field of transportation [1][2][3][4]. The real-time monitoring image information plays a pivotal role in intelligent driving [5][6][7][8][9][10][11]. In order to ensure driving safety, intelligent driving needs to make rapid judgments and responses to the surroundings, therefore the processing speed and accuracy of real-time monitoring image information is crucial [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Saliency Object Detection Method Based on Real-Time Monitoring Image Information for Intelligent Driving

Yu¹,

Qin²,

Zhang³

et al. 2023

View full text Add to dashboard Cite

Reducing traffic accident occurrences and enhancing road safety can be achieved through the processing of real-time surveillance image information for saliency object detection. Although existing saliency object detection methods based on real-time monitoring image information for intelligent driving have yielded certain results, there remain some shortcomings. In complex road environments, distinguishing between background and salient targets with existing methods proves difficult, resulting in false and missed detections. Consequently, this study investigates a saliency object detection method based on real-time monitoring image information for intelligent driving. The Visual Geometry Group (VGG) network discriminator in Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) is modified, and techniques such as spectral normalization (SN) are employed to improve the dynamic stability of training. Pixel-level image size amplification and feature enhancement are conducted on the salient objects in the dataset, providing a richer data foundation for subsequent real-time monitoring of saliency target detection and defect classification. The YOLOv5s algorithm is utilized as the identification network, and the original YOLOv5s backbone network is replaced with the MobileNetV2 network, significantly reducing network complexity and enhancing identification efficiency. The algorithm's performance in recognizing salient targets in real-time monitoring images for intelligent driving is further improved through network optimizer optimization and clustering algorithm adoption. The efficacy of the proposed method is substantiated by experimental results.

show abstract

Intelligent Assisted Driving System Based on Multi-MCU

Cited by 2 publications

References 0 publications

Autonomous landing algorithm of UAV based on Target tracking

Autonomous landing algorithm of UAV based on Target tracking

Saliency Object Detection Method Based on Real-Time Monitoring Image Information for Intelligent Driving

Contact Info

Product

Resources

About