ArticleSensors (Basel, Switzerland)2024
Research on Improved Algorithms for Cone Bucket Detection in Formula Unmanned Competition.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11.Sensors (Basel, Switzerland) · 2025Article
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4 authors.
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Abstract
The model network based on YOLOv8 for detecting race cones and buckets in the Formula Unmanned Competition for Chinese university students needs help with problems with complex structure, redundant number of parameters, and computation, significantly affecting detection efficiency. A lightweight detection model based on YOLOv8 is proposed to address these problems. The model includes improving the backbone network, neck network, and detection head, as well as introducing knowledge distillation and other techniques to construct a lightweight model. The specific improvements are as follows: firstly, the backbone network for extracting features is improved by introducing the ADown module in YOLOv9 to replace the convolution module used for downsampling in the YOLOv8 network, and secondly, the FasterBlock in FasterNet network was introduced to replace the fusion module in YOLOv8 C2f, and then the self-developed lightweight detection head was introduced to improve the detection performance while achieving lightweight. Finally, the detection performance was further improved by knowledge distillation. The experimental results on the public dataset FSACOCO show that the improved model's accuracy, recall, and average precision are 92.7%, 84.6%, and 91%, respectively. Compared with the original YOLOv8n detection model, the recall and average precision increase by 2.7 and 1.2 percentage points, the memory is half the original, and the model computation is 51%. The model significantly reduces the misdetection and leakage of conical buckets in real-vehicle tests and, at the same time, ensures the detection speed to satisfy the deployment requirements on tiny devices. Satisfies all the requirements for deployment of tiny devices in the race car of the China University Student Driverless Formula Competition. The improved method in this paper can be applied to conebucket detection in complex scenarios, and the improved idea can be carried over to the detection of other small targets.
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Registered trials
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