储粮害虫是造成粮食产后损失的重要因素，对粮食害虫早期活动进行检测和监控是减少储粮损失的必要且合适的防控措施。随着人工智能的发展，基于深度学习的图像检测方法在农业领域得到了广泛应用，目前在储粮害虫检测领域的研究相对较少，数据集的质量往往决定了深度学习模型能够学到的知识水平，因此构建专门用于储粮害虫图像检测和计数的数据集具有重要意义。本文提出的数据集GrainPest包含500幅粮虫原始图像、500幅像素级显著目标标注图像、420个害虫检测目标框标注文件和500条粮虫数量数据。数据集涵盖了玉米象、麦蛾、谷蠹、玉米螟、大谷盗、蚕豆象、米象、咖啡豆象、绿豆象、印度谷螟等主要粮食害虫，图像背景涉及小麦、玉米、大米、稻谷、绿豆、蚕豆等常见粮食。由于实际检测中有很多粮食是未感染虫害的，因此数据集还包含了80幅不含害虫目标的纯粮食背景图像，这增加了害虫显著性检测的难度。本数据集提供了一个多样性的粮虫图像基准数据集，旨在促进深度学习在储粮害虫显著性检测、目标检测和粮虫计数方面的研究，为降低粮食储藏损失和保障粮食安全提供支持。

As grain pests cause a major post-harvest loss in stored grains, early detection and monitoring of grain pest activities become necessary for applying appropriate actions to reduce storage losses. With the development of artificial intelligence, image detection methods based on deep learning have been widely used in agriculture. However, current research in stored grain pest detection is relatively limited. The quality of the dataset will determine the level of knowledge that deep learning models can learn. Therefore, constructing a specialized dataset for grain pest detection and counting is of great significance. The proposed dataset GrainPest includes 500 original images of grain insects, 500 pixel-level saliency annotation images, 420 files with insect bounding boxes and 500 entries of pest counts. The data set covers various grain pests such as corn weevil, wheat moth, grain beetle, and corn borer, as well as different types of grain backgrounds such as wheat, corn, and rice. Due to the fact that many grains are not infected with pests, the GrainPest also includes 80 pure grain background images without any pest, which bring more challenge for saliency detection. The GrainPest provides a benchmark dataset to promote the research of saliency detection, object detection, and pests counting in stored grains, and the work will provide support for reducing grain storage losses and ensuring food security.