MAFF-YOLO: a target detection model for planting holes in afforestation acceptance

Objective To solve the problems like strong subjectivity, lack of scientific basis, and insufficient personnel in traditional afforestation acceptance, this paper proposes an afforestation hole detection model of MAFF-YOLO. It aims to automatically identify and count the number and location of afforestation holes, promoting the digital transformation of afforestation acceptance and improving efficiency and scientific accuracy.

Method Based on the YOLOv8 model, MAFF-YOLO was obtained through multiple improvements. First, it used MobileNetV4 as the backbone network to increase parameters and layers, enhancing detection accuracy. Second, it added a normalization-based attention module (NAM) to better capture hole features and reduce false detections. Third, it replaced the feature fusion module with a cross-scale feature fusion module (CCFM), which integrated features of different scales and reduced computational load, improving detection of small holes. Fourth, it replaced the detection head with an RFAHead, which dynamically adjusted the receptive field based on data complexity and importance, thereby enhancing adaptability to different input features. Finally, the bounding box loss function was optimized to FocusCIoU to address sample imbalance and improve learning capability for key samples.

Result MAFF-YOLO demonstrated high accuracy in identifying the number and location of planting holes. Compared with basic YOLOv8 model, its precision increased by 1 percentage point, mAP50 by 0.7 percentage points, and F_0.5 by 0.6 percentage points. Moreover, the algorithm complexity was significantly reduced.

Conclusion Under the same experimental conditions, MAFF-YOLO shows significant advantages over other existing methods in improving the detection performance of afforestation holes. It has been successfully integrated into an end-to-end detection system, providing effective technical support for the digitalization of afforestation acceptance and further enhancing the efficiency and scientific nature of the acceptance process.