Academic Journal
Multi-Scale Target Detection in Autonomous Driving Scenarios Based on YOLOv5-AFAM
| Title: | Multi-Scale Target Detection in Autonomous Driving Scenarios Based on YOLOv5-AFAM |
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| Authors: | Hang Ma, Wei Zhao, Bosi Liu, Wenbai Chen |
| Source: | Applied Sciences, Vol 14, Iss 11, p 4633 (2024) |
| Publisher Information: | MDPI AG, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | multi-scale object detection, autonomous driving, YOLOv5, Adaptive Fusion Attention Module, Efficient Multi-scale Attention Module, MPDIou-LOSS Loss Function, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | Multi-scale object detection is critically important in complex driving environments within the field of autonomous driving. To enhance the detection accuracy of both small-scale and large-scale targets in complex autonomous driving environments, this paper proposes an improved YOLOv5-AFAM algorithm. Firstly, the Adaptive Fusion Attention Module (AFAM) and Down-sampling Module (DownC) are introduced to increase the detection precision of small targets. Secondly, the Efficient Multi-scale Attention Module (EMA) is incorporated, enabling the model to simultaneously recognize small-scale and large-scale targets. Finally, a Minimum Point Distance IoU-based Loss Function (MPDIou-LOSS) is introduced to improve the accuracy and efficiency of object detection. Experimental validation on the KITTI dataset shows that, compared to the baseline model, the improved algorithm increased precision by 2.4%, recall by 2.6%, mAP50 by 1.5%, and mAP50-90 by an impressive 4.8%. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/14/11/4633; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app14114633 |
| Access URL: | https://doaj.org/article/10765737f3164a78af19c537979997fd |
| Accession Number: | edsdoj.10765737f3164a78af19c537979997fd |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20763417 |
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| DOI: | 10.3390/app14114633 |
| Published in: | Applied Sciences |
| Language: | English |