Detecting small targets in large fields of view is a challenging task. Nowadays, many targets detection models based on the convolutional neural network (CNN) achieve excellent performance. However, these CNN-based detectors are inefficient when applied to tasks of real-time detection of small targets. This paper proposes a small-target detection model in large fields of view based on the tectofugal–thalamofugal–accessory optic system of birds. Within this model, first, we design an unsupervised saliency algorithm to generate saliency regions to suppress background information according to the visual information processing mechanism of the tectofugal pathway of birds. Second, we design a super-resolution (SR) analysis method to enlarge small targets and improve image resolution by the information processing mechanism of the accessory optic system of birds. Then, according to the information processing mechanism of the thalamofugal pathway, we propose a CNN-based method to detect small targets. We further test our model on two public datasets (the VEDAI dataset and DLR 3 K dataset), and the experimental results demonstrate that the proposed detection model outperforms the state-of-the-art methods on small-target detection.

在大视野中检测小目标是一项艰巨的任务。如今，许多基于卷积神经网络（CNN）的目标检测模型都取得了出色的性能。但是，这些基于CNN的探测器在应用于小目标的实时检测任务时效率低下。本文提出了一种基于鸟类的远视-丘脑-远视-附件光学系统的小目标检测模型。在该模型中，首先，我们设计了一种无监督的显着性算法，以根据鸟类的构造体途径的视觉信息处理机制生成显着性区域来抑制背景信息。第二，我们设计了一种超分辨率（SR）分析方法，通过鸟类辅助光学系统的信息处理机制来扩大小目标并提高图像分辨率。然后，根据丘脑真菌通路的信息处理机制，提出了一种基于CNN的小目标检测方法。我们在两个公共数据集（VEDAI数据集和DLR 3 K数据集）上进一步测试了我们的模型，实验结果表明，所提出的检测模型优于小目标检测的最新方法。