EMAF-Net: A Lightweight Single-Stage Detector for 13-Class Object Detection in Agricultural Rural Road Scenes

Rural road perception for agricultural machinery automation faces challenges including complex backgrounds, drastic lighting and weather variations, frequent occlusions, and high densities of small objects with significant scale variations. These factors make conventional detectors prone to missed detections and misclassifications. To address these issues, a 4K rural road dataset with 4771 images is constructed. The dataset covers 13 object categories and includes diverse day/night conditions and multiple weather scenarios on both structured and unstructured roads. EMAF-Net, a lightweight single-stage detector based on YOLOv4-P6, is proposed. The backbone integrates an EMHA module combining EfficientNet-B1 with multi-head self-attention (MHSA) for enhanced global context modeling while preserving efficient local feature extraction. The neck adopts an Improved ASPP and a bidirectional FPN to achieve robust multi-scale feature fusion and expanded receptive fields. Meanwhile, CIoU loss is used to optimize bounding box regression accuracy. The experimental results demonstrate that EMAF-Net achieves an mAP@0.5 of 64.05% and an mAP@0.5:0.95 of 48.95% on a rural road dataset. At the same time, it maintains a lightweight design with 18.3 M parameters and a computational complexity of 38.5 GFLOPs. Ablation studies confirm the EMHA module contributes a 6.22% mAP@0.5 improvement, validating EMAF-Net's effectiveness for real-time rural road perception in autonomous agricultural systems.