摘要： 针对资源受限场景中多尺度目标检测模型难以兼顾高精度与低参数量、低计算复杂度的问题，提出了一种基于YOLOv8的边缘端轻量级多尺度目标检测方法。首先，在主干网络的跨阶段部分双特征融合模块中嵌入了自主设计的轻量级通道-空间注意力模块，通过融合高效通道注意力机制和多尺度深度可分离低秩卷积，在模块复杂度较低的同时实现通道和空间双维度特征增强。其次，设计了跨层自适应加权融合模块，建立跨层连接，通过自适应加权机制融合浅层细节特征与深层语义信息。再次，将颈部网络跨阶段部分双特征融合模块中的瓶颈结构替换为通用倒置瓶颈，在维持检测精度的同时降低计算复杂度。最后，提出了聚焦式尺度自适应动态交并比损失，通过尺度自适应调制项与聚焦机制，动态调整误差惩罚力度并强化对难检测目标的关注。在BDD100K数据集上，与当前先进的YOLO11m模型相比，LMS-YOLO-m在mAP@50与mAP分别提升了0.5%和0.1%，参数量减少了2.4%，计算量降低了5.8%，结果表明该方法在检测精度更高的同时具有更低的参数量和更低的计算复杂度。

Abstract: To address the challenge of balancing high accuracy with low parameter count and computational complexity in multi-scale object detection for resource-constrained edge scenarios, this paper proposes LMS-YOLO, a lightweight multi-scale detection method based on YOLOv8. The approach introduces four key innovations: a lightweight channel-spatial attention module integrated into the backbone's CSP blocks that combines efficient channel attention with multi-scale depthwise separable low-rank convolutions for dual-dimensional feature enhancement; a cross-layer adaptive weighted fusion module establishing skip connections to dynamically integrate shallow detail features with deep semantic information; replacement of standard bottlenecks with generalized inverted bottlenecks in the neck network to reduce computation while maintaining accuracy; and a novel focal scale-aware dynamic IoU loss that adaptively adjusts error penalties based on target scale and detection difficulty. Comprehensive evaluations on the BDD100K dataset demonstrate that LMS-YOLO-m achieves superior performance compared to YOLOv8m, with 0.5% and 0.1% improvements in mAP@50 and mAP respectively, while reducing parameters by 2.4% and computation by 5.8%, making it particularly suitable for deployment in resource-constrained edge computing environments where efficiency and accuracy must be carefully balanced.