## 1 Introduction  
Tracking object movement technology is one of the key technologies in visual computing and has been extensively and successfully employed in many fields, such as security surveillance [1], autonomous driving [2], and early warning systems. Although it can effectively detect object motion in various complex scenes, there remains a great challenge in accurately distinguishing small target motion in complex dynamic environments due to low-resolution and limited visual features. For example, when an object is located at a great distance from the observing camera, it occupies only a few pixels and lacks distinct physical characteristics in the captured image, making it challenging to extract the limited features of such small objects (see Figure 1a). Moreover, in the real visual environment, the small object is usually concealed in complex backgrounds, which further complicates the discrimination of the small target from small-target-like objects (such as leaves, branches, and so on) in backgrounds. Therefore, developing a robust visual system that can recognize the movement of small objects in complex dynamic environments is an urgent problem for future computer vision systems. Over the past few decades, researchers have proposed a variety of algorithms to address the problem of object motion detection. Traditional computer vision methods (such as background subtraction [3; 4], temporal differencing [5], and optical flow [6]) perform well in static backgrounds, but they have limitations in detecting targets, especially small targets, in complex dynamic scenes. Deep learning methods have made significant progress in object classification [7; 8; 9] and salient object detection [10; 11], but due to the inconspicuous visual features of small targets, their detection performance is not satisfactory. Biological studies have found that some insects, such as dragonflies, can perfectly track flying small objects in complex natural environments by using limited visual information and the successful rates of pursuit are greater than 97% [12]. This superior ability is due to a special type of neurons in the visual nervous system of insects called small target motion detectors (STMDs), which present a strong sensitivity to small moving objects [13; 14]. The perceptual neurons respond powerfully to small moving objects covering 1 degree to 3 degree of the perceptual field, but show weak or no response to larger moving objects  
Figure 1: (**a**) When an object is located at a significant distance from the imaging device, it occupies just a single pixel or a small number of pixels and lacks distinct physical features. The white circle of the image represents the small target (flying plane), and the top right corner red circle of the image represents the larger view of the small target. (**b**) The diagram of neuron information connection of the proposed visual network. The proposed visual network involves two parts, where one is the motion information detection channel, and the other is the contrast information detection channel. The external visual information is processed via ommatidia, LMC, TM3, TM1, and STMD neurons for detecting small targets’ motion in the motion information detection channel. Meanwhile, the contrast information of the object is extracted by AMC and T1 neurons in the contrast information detection pathway. Integrating contrast information and motion information in STMD neurons allows the detection of small objects’ motion. exceeding 10 degrees, and to large stimuli [15]. Further research indicates that these unique neurons are equally sensitive to small moving objects in complicated dynamic environments. Therefore, the special visual system of tracking small targets with precision in an insect's brain may provide a reliable template for designing a robust small target detection visual neural network.