In complex road environments, existing algorithms for road multi-target detection suffer from poor recognition performance, large number of parameters, and high computational complexity, making them unsuitable for deployment on resource-limited mobile devices. To address these issues, a lightweight road multi-target detection algorithm combining non-adjacent features is proposed based on YOLOv7-tiny. First, the design of the Tiny-AFPN combines non-adjacent features of different scales, reducing the loss of features caused by scale differences and achieving richer cross-scale information interaction. Secondly, with the introduction of DSConv, the Efficient Layer Aggregation Network was redesigned and named ELAN-DS, improving the expression of features while optimizing the efficient layer aggregation network and reducing the complexity of the model. Finally, the use of the MPDIoU loss function improves the accuracy of bounding box