为解决自动驾驶感知域控制器中障碍物检测模型对高内存和高计算资源需求的问题，提出了一种基于YOLOv8改进的轻量化障碍物检测方法，使用内存访问和计算量更少的FasterNet重构YOLOv8主干网络。为弥补模型轻量化导致的精度下降以及对小目标检测能力的不足，主要在3个方面对YOLOv8进行改进：用SPD-Conv（space-to-depth convolution）替换颈部网络的传统跨步卷积，增强小目标特征提取能力；结合IIoU（inner IoU）和PIoU（powerful IoU）的思想，提出IPIoU（inner powerful IoU）作为边框回归损失，加快损失函数收敛并提高小目标检测性能；引入注意力机制SimAM（simple attention module），进一步提高模型检测精度。实验结果表明，改进模型相比原模型在参数量、计算量和模型大小分别降低29.1%、20.5%和28.8%的情况下，检测精度提升了1.2%。模型部署至自动驾驶车载控制器后，能够有效检测道路前方障碍物。

To solve the problem of high memory and computational resource demands in obstacle detection models within autonomous driving perception domain controllers, a lightweight obstacle detection method based on improved YOLOv8 was proposed. This method reconstructs the YOLOv8 backbone network using FasterNet, which utilizes less memory access and computational resources. To mitigate the accuracy decline and the insufficient detection capabilities for small objects caused by model lightweighting, three main improvements were made to YOLOv8: SPD-Conv (space-to-depth convolution) was used to replace traditional stride convolution in the neck network to enhance small object feature extraction. IPIoU(inner powerful IoU), combining the concepts of IIoU(inner IoU) and PIoU(powerful IoU), is introduced as the bounding box regression loss to accelerate loss convergence and improve small object detection performance. SimAM (simple attention module) was incorporated to further enhance model detection accuracy. Experimental results demonstrate that, compared to the original model, the improved model achieves a reduction of 29.1% in parameters, 20.5% in computational load, and 28.8% in model size, while increasing mAP@0.5 by 1.2%. Once deployed in autonomous driving vehicle controllers, the model effectively detects obstacles on the road ahead.