A Study on the Performance Improvement of a Conical Bucket Detection Algorithm Based on YOLOv8s

Abstract

In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order to cope with these problems, the thesis is based on YOLOv8s to improve the cone bucket detection algorithm. Firstly, a P2 detection layer for detecting tiny objects is added on top of YOLOv8s to detect small targets with 160 × 160 pixels, which improves the detection of small conical buckets in the distant view. At the same time, to reduce the network’s complexity to achieve lightweightness, the original 20 × 20 pixel detection header is deleted. Second, the head of the original YOLOv8 is replaced with a multi-scale fusion Dynamic Head, designed to improve the head’s ability in scale, space, and task perception to enhance the detection performance of the model in complex scenes. Again, a novel loss function, MPDIoU, is introduced, which has advantages in simplifying the bounding box similarity comparison, and it can adapt to the overlapping or non-overlapping situation of the bounding box more effectively. It reduces the phenomenon of missed detection caused by overlapping conical buckets. Finally, the LAMP pruning method is used to trim the model to make the model lightweight. By adding and modifying the above modules, the improved algorithm improves the detection accuracy from 92.2% to 95.2%, the recall rate from 84.2% to 91.8%, and the average accuracy from 91.3% to 96%, while the number of parameters is reduced from 28.7 M to 26.6 M. The detection speed still meets the real-time requirement in real-vehicle testing compared to the original algorithm. In the real car test, compared with the original algorithm, the improved algorithm shows apparent advantages in reducing the missed detection of cones and barrels, which meets the demand for high accuracy of cones and barrel detection in the complex race environment and also meets the conditions for deployment on small devices with limited resources.