To address the issues of disorderly distribution of rockfalls and difficult identification of small fragmented rocks on mountain roads, a rockfall detection model for mountain roads based on the YOLOv8 (you only look once version 8) framework was proposed. The feature fusion strategy and feature extraction structure of the PPA (parallelized patch-aware attention) module were improved to design the EFF-PPA (enhance feature fusion-PPA) module. This module was used to optimize and reconstruct the main convolution module of the base model, resulting in the EFP-C2f module, which retains richer feature information. Based on the characteristics of the dataset, a large-target detection layer was added to the neck network to improve the detection accuracy for large targets. A LMSA(lightweight multi-scale adaptability) detection head with a partially decoupled structure was proposed. This head consists of an EMSConv (efficient multi-scale conv) module using group normalization and a shared-parameter convolution separated from the group normalization layer. The detection accuracy was improved while appropriate lightweighting was performed. In the self-constructed rockfall dataset, the average precisions of the baseline model and the improved model are achieved at 87.7% and 89.8%, respectively. Higher accuracy is attained by the improved model while maintaining a high detection speed, with a frame rate reaching 212.1 frames per second. In different scene tasks collected in the field, various types of targets can be accurately recognized by the improved model, showing good generalization ability. Currently, limited research is conducted on rockfall detection on mountain roads, and the problem-oriented model improvement approach adopted in this design is considered to provide valuable reference.