Small object detection techniques have been developed for decades, but one of key remaining open challenges is detecting tiny objects in wild or nature scenes. While recent works on deep learning techniques have shown a promising potential direction on common object detection in the wild, their accuracy and robustness on tiny object detection in the wild are still unsatisfactory. In this paper, we target at studying the problem of tiny pest detection in the wild and propose a new effective deep learning approach. It builds up a global activated feature pyramid network on convolutional neural network backbone for detecting tiny pests across a large range of scales over both positions and pyramid levels. The network enables retrieving the depth and spatial intension information over different levels in the feature pyramid. It makes variance or changes of spatial or depth-sensitive features in tiny pest images more visible. Besides, a hard example enhancement strategy is also proposed to implement fast and efficient training in this approach. The approach is evaluated on our newly built large-scale wide tiny pest dataset containing 27.8K images with 145.6K manually labelled pest objects. The results show that our approach perform well on pest detection with over 71% mAP, which outweighs other state-of-the-art object detection methods.

小物体检测技术已经发展了几十年，但仍然存在的主要挑战之一是检测野生或自然场景中的小物体。虽然最近关于深度学习技术的工作已经在野外常见物体检测方面显示出有希望的潜在方向，但它们在野外微小物体检测上的准确性和鲁棒性仍然不能令人满意。在本文中，我们旨在研究野外微小害虫的检测问题，并提出一种新的有效深度学习方法。它在卷积神经网络主干上建立了一个全局激活的特征金字塔网络，用于在位置和金字塔级别的大范围内检测微小的害虫。该网络能够检索特征金字塔中不同级别的深度和空间强度信息。它使微小害虫图像中空间或深度敏感特征的变化或变化更加明显。此外，还提出了一种硬样本增强策略，以在该方法中实现快速高效的训练。该方法是在我们新建的包含 27.8K 图像和 145.6K 手动标记的害虫对象的大规模宽小害虫数据集上进行评估的。结果表明，我们的方法在有害生物检测方面表现良好，mAP 超过 71%，超过了其他最先进的对象检测方法。6K 手动标记的害虫对象。结果表明，我们的方法在有害生物检测方面表现良好，mAP 超过 71%，超过了其他最先进的对象检测方法。6K 手动标记的害虫对象。结果表明，我们的方法在有害生物检测方面表现良好，mAP 超过 71%，超过了其他最先进的对象检测方法。