改进YOLOv8提升车辆检测精度与速度，适用于复杂道路环境。
Research on vehicle detection based on improved YOLOv8 network
- 用FasterNet替换主干网络，降低计算量并提升速度
- 添加CBAM注意力机制增强特征表达，提升小目标检测
- 改进损失函数WIoU，优化定位精度和分割效果
自动驾驶系统安全避障的关键在于高精度的车辆识别。然而，实际道路环境多变，车辆与行人特征多样，严重制约检测精度提升。本文提出一种改进的YOLOv8车辆检测方法：以YOLOv8n-seg为基础，首先用FasterNet替换主干网络，在降低计算复杂度和内存占用的同时提升检测精度与速度；其次在Neck部分引入CBAM注意力机制，增强特征表达能力；最后将损失函数CIoU改进为WIoU，优化检测框定位并提升分割准确率。实验结果表明，改进模型在汽车、行人、摩托车上的检测准确率分别达到98.3%、89.1%和88.4%，在六项指标（如Precision）上优于原模型及YOLOv9。
原文摘要 · Abstract (English)
The key to ensuring the safe obstacle avoidance function of autonomous driving systems lies in the use of extremely accurate vehicle recognition techniques. However, the variability of the actual road environment and the diverse characteristics of vehicles and pedestrians together constitute a huge obstacle to improving detection accuracy, posing a serious challenge to the realization of this goal. To address the above issues, this paper proposes an improved YOLOv8 vehicle detection method. Specifically, taking the YOLOv8n-seg model as the base model, firstly, the FasterNet network is used to replace the backbone network to achieve the purpose of reducing the computational complexity and memory while improving the detection accuracy and speed; secondly, the feature enhancement is achieved by adding the attention mechanism CBAM to the Neck; and lastly, the loss function CIoU is modified to WIoU, which optimizes the detection box localization while improving the segmentation accuracy. The results show that the improved model achieves 98.3%, 89.1% and 88.4% detection accuracy for car, Person and Motorcycle. Compared with the pre-improvement and YOLOv9 models in six metrics such as Precision.
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