To solve the problem of low accuracy of existing unmanned target detection algorithm caused by object occlusion and small-object information loss in complex road scenarios, this paper proposes an enhanced obstacle detection algorithm based on YOLOv8, named YOLOv8-EA (effectual accurate). The algorithm incorporates a lightweight backbone using partial convolution to preserve spatial feature integrity. A large-kernel depthwise convolutional layer is introduced to reconstruct the pyramid pooling structure, and the multi-scaled self-attention features are fused through parallel connections, which Enhances the feature extraction ability of the model in complex scenarios. Additionally, a multi-branch architecture with reparameterization suppresses noise interference and enhances feature fusion via stacked gradient flow. A small-object detection head based on partial convolution is also designed to improve pixel-level feature extraction for small targets. Experimental results show that YOLOv8-EA achieves notable improvement in detection accuracy compared to the original YOLOv8. On the KITTI dataset, mAP50 and mAP50-95 increased by 2.4% and 4.7%, respectively, while on the SODA10M dataset, gains of 1.4% and 1.1% were observed, which demonstrate the strong generalization ability of YOLOv8-EA. The proposed algorithm shows superior capability in handling occlusion and small-object detection, offering more reliable perception support for unmanned driving systems.