Autonomous driving paper index
An Enhanced YOLOv8 Algorithm with C2f_Parnet for Vehicle and Pedestrian Detection
One-line summary
This paper proposes enhancements to the YOLOv8s model for autonomous driving object detection tasks.
Engineering notes
Key topics: autonomous driving, object detection, kitti. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This paper proposes enhancements to the YOLOv8s model for autonomous driving object detection tasks. Firstly, a C2f Parnet module is designed based on the Parnet parallel network architecture to reduce network depth and computational latency. Secondly, GSConv is modified by employing dual 3 × 3 convolutions to decrease parameter count. Thirdly, L2-scored structural pruning is introduced to compress redundant channels. Finally, detection accuracy is improved using EIoU loss. Experiments on the KITTI dataset demonstrate that the improved model maintains a mAP50 of 0.917. The performance of this model is almost comparable to the original model (0.939), but with parameters reduced to 11 million and GFLOPs decreased to 26.2. Compared with other methods, L2 pruning achieved the highest mAP50-95 score of 0.753. In complex traffic environments, this model demonstrates robust real-time detection capabilities, can be flexibly deployed, and is a useful choice for lightweight detection in autonomous driving.
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