YOLOv8-QSD: An Improved Small Object Detection Algorithm for Autonomous Vehicles Based on YOLOv8

As self-driving vehicles become more prevalent, the speed and accuracy of detecting surrounding objects through onboard sensing technology have become increasingly important. The YOLOv8-QSD network is a novel anchor-free driving scene detection network that builds on YOLOv8 and ensures detection acc...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 16
Hlavní autoři: Wang, Hai, Liu, Chenyu, Cai, Yingfeng, Chen, Long, Li, Yicheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
2-D object detection
Accuracy
Algorithms
Autonomous cars
Autonomous driving
Commercial vehicles
Feature extraction
highway traffic
Image recognition
Location awareness
Object detection
Object recognition
Safety
YOLO
ISSN:0018-9456, 1557-9662
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Shrnutí:As self-driving vehicles become more prevalent, the speed and accuracy of detecting surrounding objects through onboard sensing technology have become increasingly important. The YOLOv8-QSD network is a novel anchor-free driving scene detection network that builds on YOLOv8 and ensures detection accuracy while maintaining efficiency. The network's backbone employs structural reparameterization techniques to transform the diverse branch block (DBB)-based model. To accurately detect small objects, it integrates features of different scales and implements a bidirectional feature pyramid network (BiFPN)-based feature pyramid after the backbone. To address the challenge of long-range detection in driving scenarios, a query-based model with a new pipeline structure is introduced. The test results demonstrate that this algorithm outperforms YOLOv8 on the large-scale small object detection dataset (SODA-A) in terms of both speed and accuracy. With an accuracy rate of 64.5% and reduced computational requirements of 7.1 GFLOPs, it satisfies the speed, precision, and cost-effectiveness requirements for commercial vehicles in high-speed road driving scenarios.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3379090