Monocular visual obstacle avoidance method for autonomous vehicles based on YOLOv5 in multi lane scenes

wang, Junhua; Han, Laiquan; Jiang, Yuan; Qi, Yongjun; Altangerel, Khuder

doi:10.1016/j.aej.2024.09.019

Alexandria Engineering Journal

2024

DOI: 10.1016/j.aej.2024.09.019

|View full text |Cite

Monocular visual obstacle avoidance method for autonomous vehicles based on YOLOv5 in multi lane scenes

Junhua wang,

Laiquan Han,

Yuan Jiang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Target Detection in Remote Sensing Images Based on Recognition Algorithm

Zhang

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Aiming at the problem of low target detection accuracy caused by the close arrangement of targets, complex feature information, and numerous small targets in remote sensing images, this paper proposes a target detection algorithm based on the improved Yolov5 algorithm for remote sensing images. Firstly, this paper designs a remote sensing image target detection algorithm C²-YOLO based on multi-level feature fusion. Combined with the attention mechanism module to focus on the spatial location information of the target, which improves the extraction effect of the features of the target of interest. The network uses the CARAFE upsampling operator with a larger sensing field to better utilize the surrounding information. The proposal is to incorporate a predictive header hl that is generated from a low-level, high-resolution feature map to improve the recognition of tiny objects. The problem of large target scales in remote sensing images and poor detection of small targets has been improved. Angular loss is also added to the loss function to improve the detection of rotating targets. Finally, the accuracy comparison and experimental effect visualization are carried out on the dataset, and the experimental results show that the improvement work in this paper achieves better results no matter comparing the original YOLOv5 detection algorithm or other types of detection algorithms. Specifically, the accuracy of C²-YOLO on the NWPU dataset 1-shot recognition task is 78.45%, which is much higher than that of other detection algorithms, and is able to reach a superior performance in the remote sensing image target detection task.

show abstract

Target Detection in Remote Sensing Images Based on Recognition Algorithm

Zhang

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Monocular visual obstacle avoidance method for autonomous vehicles based on YOLOv5 in multi lane scenes

Cited by 1 publication

References 29 publications

Target Detection in Remote Sensing Images Based on Recognition Algorithm

Target Detection in Remote Sensing Images Based on Recognition Algorithm

Contact Info

Product

Resources

About