YOLOv13-Cone-Lite: An Enhanced Algorithm for Traffic Cone Detection in Autonomous Formula Racing Cars
Source abstract
Abstract
This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP50 of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Autonomous Vehicle Technology and Safety · Vehicle License Plate Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automobile, Chang’an University, Xi’an 710064, China
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
School of Construction Machinery, Chang’an University, Xi’an 710064, China
School of Continuing Education, Chang’an University, Xi’an 710064, China
School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
Funding
National Natural Science Foundation of China · Key Research and Development Program of Shaanxi Province · Fundamental Research Funds for the Central Universities CHD
References (40)
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