Rana Shubham, Gerbino Salvatore, Crimaldi Mariano, Cirillo Valerio, Carillo Petronia, Sarghini Fabrizio, Maggio Albino
Department of Engineering, University of Campania "L. Vanvitelli", Via Roma 29, 81031 Aversa, Italy.
Department of Agricultural Sciences, University of Naples "Federico II", Via Università 100, 80055 Naples, Italy.
J Imaging. 2024 Feb 29;10(3):61. doi: 10.3390/jimaging10030061.
This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing crop and weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.
本文聚焦于基于尺度不变特征变换（SIFT）和随机抽样一致性（RANSAC）的多光谱（MS）图像配准中巷道的综合评估。本文的思路是在包含作物和杂草的异质混合物上广泛评估三种基于SIFT和RANSAC的配准方法。第一种方法基于单应性矩阵的应用，该矩阵是在MS图像与各个注释的空间坐标配准过程中得出的，以实现空间重新对齐。第二种方法基于从各个光谱通道的地面真值导出的二进制掩码的配准。第三种方法基于各个光谱通道中仅感兴趣的掩码像素的配准。研究发现，基于从手动分割图像导出的二进制掩码配准的MS图像配准技术表现出最高的准确性，其次是涉及掩码像素配准的技术，最后是基于注释空间重新对齐的配准。在自动分割图像中，基于自动预测掩码实例配准的技术比基于掩码像素配准的技术具有更高的准确性。在地面真值图像中，通过近红外通道进行的注释具有更高的准确性，其次是绿色、蓝色和红色光谱通道。在自动分割图像中，观察到蓝色通道的准确性较高，其次是绿色、近红外和红色通道。在单个实例级别，基于二进制掩码的配准在绿色通道中表现出最高的准确性，其次是基于红色通道中掩码像素配准的方法，最后是基于绿色通道中注释空间重新对齐的方法。使用YOLOv8l-seg对野生萝卜进行实例检测时，其在0.5的平均精度均值（mAP）为92.11%，对其二进制掩码实例进行分割的精度为98%。
