Multi-scale object detection is critically important in complex driving environments within the field of autonomous driving. To enhance the detection accuracy of both small-scale and large-scale targets in complex autonomous driving environments, this paper proposes an improved YOLOv5-AFAM algorithm. Firstly, the Adaptive Fusion Attention Module (AFAM) and Down-sampling Module (DownC) are introduced to increase the detection precision of small targets. Secondly, the Efficient Multi-scale Attention Module (EMA) is incorporated, enabling the model to simultaneously recognize small-scale and large-scale targets. Finally, a Minimum Point Distance IoU-based Loss Function (MPDIou-LOSS) is introduced to improve the accuracy and efficiency of object detection. Experimental validation on the KITTI dataset shows that, compared to the baseline model, the improved algorithm increased precision by 2.4%, recall by 2.6%, mAP50 by 1.5%, and mAP50-90 by an impressive 4.8%.

This research presents an advanced YOLOv8-MMD framework specifically designed for intelligent white radish harvesting systems, addressing the critical need for simultaneous species recognition and quality evaluation. The proposed architecture is built upon a dual-branch detection system (YOLOv8-Dual) with a shared Backbone network, and is further enhanced by two novel components: the Multi-Scale Attention Aggregation (MSAA) module that strategically combines channel-wise and spatial attention mechanisms to refine feature representation, and the Multi-scale Feature Enhancement (MAFE) module that facilitates effective information fusion across different hierarchical levels of the network. Extensive experimental validation reveals that the YOLOv8-MMD model achieves remarkable performance metrics, including a species detection precision of 0.945 and a quality assessment precision of 0.812, representing improvements of 1.4% and 4%, respectively, over the baseline YOLOv8-Dual model. Under the comprehensive mAP@50 evaluation standard, the model reaches 0.949 for species identification and 0.859 for quality classification, while maintaining impressive recall rates of 0.924 and 0.836 for the respective tasks. The system demonstrates exceptional robustness when deployed in challenging field conditions, consistently performing well under varying lighting intensities, different growth stages, and partial occlusion scenarios. Computational analysis confirms the model’s practical viability, achieving a processing throughput of 112 frames per second with 8.1 GFLOPs of computational overhead, thereby meeting stringent real-time operational requirements for agricultural robotic applications. Comparative studies with existing methods further substantiate the superiority of the proposed approach in balancing detection accuracy with computational efficiency. The integration of multi-scale attention mechanisms and hierarchical feature enhancement strategies provides a comprehensive solution for automated agricultural harvesting in complex, unstructured environments, offering significant potential for practical implementation in precision agriculture systems.