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

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 16
• Main Authors: Wang, Hai, Liu, Chenyu, Cai, Yingfeng, Chen, Long, Li, Yicheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TIM.2024.3379090

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.