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 ELAN 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 regression and enhances the network′s target detection capabilities. In the experiments on SODA10M, compared with the original YOLOv7-tiny model, the improved algorithm increased accuracy, mAP@0.5, and recall by 1.4%, 1.4%, and 5.9%, respectively. It also reduced the number of parameters and computation by 8.2% and 41.5%, respectively. This effectively reduces the number of parameters and the computational complexity, substantially improves the detection speed of the model, and provides the possibility for deployment on edge devices.