【摘要】 药用植物目标检测可以有效应用于药用植物的图像识别以及图像的语义分割。文章对已有的算法做了优化,使其对自然环境下的药用植物目标检测更为准确,提出了一种基于选择性搜索的目标检测算法。该算法首先对药植图像进行高斯滤波去噪,并对图像做归一化预处理。对预处理后的图片使用基于图的图像分割算法进行原始分割区域的划分,计算相邻区域间的颜色、纹理、大小和交叠相似度。最后根据相似度进行区域合并,最终得到目标区域。文章图片数据集来自PPBC中国植物图像库以及作者实地拍摄。实验结果表明,该算法对有花植物检测得分达到84.3,叶片植物检测得分达到67.86,平均检测得分为76.08,较原选择性搜索算法提升12.64。此外,该算法不需要训练,计算简单,适用性更强。更多还原

【Abstract】 Target detection of medicinal plants can be effectively applied to image recognition and semantic segmentation of medicinal plants. This article optimizes the existing algorithms to make it more accurate for the medicinal plant target detection in the natural environment, and proposes a target detection algorithm based on selective search. The algorithm first performs Gaussian filtering and denoising on the medicine plant image, and performs normalized preprocessing on the image. The pre-processed picture is divided into the original segmentation regions using a graph-based image segmentation algorithm, and the color, texture, size, and overlap similarity between adjacent regions are calculated. Finally, the region is merged according to the similarity, and the target region is finally obtained. The article picture data set is from the PPBC China Plant Image Library and the author field shooting. Experimental results show that the algorithm achieves a score of 84.3 for flowering plants, a leaf plant detection score of 67.86, and an average detection score of 76.08, which is 12.64 higher than the original selective search algorithm. In addition, the algorithm requires no training, is simple to calculate, and more applicable.更多还原