YOLOv-MA: A High-Precision Foreign Object Detection Algorithm for Rice

Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consumer safety, this study innovatively proposes a YOLOv-MA-based foreign object detection algorithm for rice, leveraging deep learning techniques. The proposed algorithm adaptively enhances multi-scale feature representation across small, medium, and large object detection layers by incorporating the multi-scale dilated attention (MSDA) mechanism. Additionally, the adaptive spatial feature fusion (ASFF) module is employed to improve multi-scale feature fusion in rice foreign object detection, significantly boosting YOLOv8’s object detection capability in complex scenarios. Compared to the original YOLOv8 model, the improved YOLOv-MA model achieves performance gains of 3%, 3.5%, 2%, 3.9%, and 4.2% in mean Average Precision (mAP @[0.5:0:95]) for clods, corn, screws, stones, and wheat, respectively. The overall mAP @[0.5:0:95] reaches 90.8%, reflecting an improvement of 3.3%. Furthermore, the proposed model outperforms SSD, FCOS, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv11, and YOLOv12 in overall performance. Thus, this model not only reduces the burden of manual inspection but also provides an efficient and high-precision solution for rice foreign object detection.