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 |
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2024
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Li, Yicheng Chen, Long Wang, Hai Liu, Chenyu Cai, Yingfeng |
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| SubjectTerms | 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 |
| Title | YOLOv8-QSD: An Improved Small Object Detection Algorithm for Autonomous Vehicles Based on YOLOv8 |
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