基于深度学习的乱占耕地违法行为监管方法OA

In response to the drawbacks of conventional methods for the routine monitoring of illegal occupation of arable land,such as high workload and poor timeliness,this study was based on dynamically updated high-resolution remote sens-ing images combined with unmanned aerial vehicle(UAV)intelligent inspection imagery.A high-quality sample library was constructed using manual labeling methods.Several deep learning models were trained,including the pyramid scene parsing network(PSPNet),deep semantic segmentation algorithm(DeepLabV3+),real-time object detection algorithm(YOLOv8),and the convolutional networks plus for biomedical image segmentation(U-net++),to conduct dynamic super-vision of illegal occupation of arable land.The results show that the PSPNet model has relatively low detection accuracy and poor completeness in object extraction.Although DeepLabV3+and YOLOv8 models exhibit higher accuracy,they struggle with the completeness of complex object extraction and severe deformation of contour boundaries.The U-Net++model shows significant advantages in both detection accuracy and object extraction completeness,achieving a detection accuracy of up to 94.7%in practical applications,thereby providing accurate and reliable data for the enforcement of regulations against illegal occupation of arable land.