## 1 Introduction
As the posterior neurons of the ommatidia, LMC cells [32; 33] have been confirmed to receive information from the ommatidia outputs and extract the motion information cue from ommatidia outputs. However, the mechanism of another type of amacrine cells (AMCs) [34; 35], which are connected to the ommatidia by slender fibers, in the visual system for detecting movements of small objects is currently unclear. Cumulative research confirms that integrating contrast information into the visual system may be effective in discriminating moving objects from distracting background features. Therefore, a contrast information processing module may be built by the AMCs and their downstream neurons to distinguish small objects from false background features. In this paper, we develop a new visual neural network called \(\mathcal{CI}\)STMD to detect and distinguish small moving objects in complex dynamic backgrounds. The visual neural network mainly consists of two channels; one is a motion information detection channel, and the other one is a local contrast information detection channel. The motion information detection channel is capable of extracting the motion information of small objects. When a small target passes over a pixel, the output of the ommatidia will show a trend of first decreasing and then increasing, and finally return to the initial level. These changes are caused by the entry and exit of the small target, respectively. LMC cells can sense the changes in brightness and pass the signal to Tm3 and Tm1 neurons. Among them, the response of Tm1 neurons has a certain delay. Finally, STMD produces a strong response by multiplying the outputs of Tm3 and Tm1 neurons at the same time position (see Figure 2a). Meanwhile, the contrast information detection channel uses AMC neurons to calculate the sum of the brightness values of the local area centered on each pixel and its surrounding area. Subsequently, T1 neurons calculate the difference in average brightness values between these two areas, thereby extracting the contrast information of each central local area (see Figure 2b). In the information fusion stage, the system integrates the information extracted by the motion detection channel and the contrast feature recognition channel. By obtaining the contrast trajectory of the target over time through its motion trajectory, the system finally calculates the variance of contrast and compares it with a threshold. This enables the system to effectively suppress background noise and achieve the precise localization of small targets (see Figure 3). The experimental findings support the notion that the proposed network can accurately distinguish small objects from background interference, and the \(\mathcal{CI}\)STMD system demonstrates significant performance in detecting small objects in complex dynamic scenes. The contributions of this paper are as follows:  
(1) This paper points out that moving small targets and background false positives exhibit different temporal contrast variation trends. Specifically, the contrast of background false positives is relatively stable, while that of small targets fluctuates significantly. (2) A novel visual neural network based on contrast features has been proposed for detecting small moving targets and suppressing background false positives. (3) The experimental results indicate that the proposed \(\mathcal{CI}\)STMD visual network achieves better performance than comparable state-of-the-art algorithms. The presentation of this article is structured as follows. A review of the pertinent literature is presented in Section 2. Our visual system is elaborated upon in Section 3. Sections 4 and 5 detail a wide range of experimental outcomes to examine the efficiency and behavior of our visual system. Lastly, Section 6 provides a summary of the findings. Figure 2: **(a)** The working mechanism of the motion information detection channel. When a small target passes through a pixel, the ommatidium output first decreases and then increases, eventually returning to its initial level.