A Bio-Inspired Visual Network That Fuses Motion and Contrast Features for Detecting Small Moving Objects in Dynamic Complex Environments

In complex and dynamic environments, traditional motion detection techniques that rely on visual feature extraction face significant challenges when detecting and tracking small-sized moving objects. These difficulties primarily stem from the limited feature information inherent in small objects and the substantial interference caused by irrelevant information in complex backgrounds. Inspired by the intricate mechanisms for detecting small moving objects in insect brains, some bio-inspired systems have been designed to identify small moving objects in dynamic natural backgrounds. While these insect-inspired systems can effectively utilize motion information for object detection, they still suffer from limitations in suppressing complex background interference and accurately segmenting small objects, leading to a high rate of false positives from the complex background in their detection results. To overcome this limitation, inspired by insect visual neural structures, we propose a novel dual-channel visual network. The network first utilizes a motion detection channel to extract the target’s motion position information and track its trajectory. Simultaneously, a contrast detection channel extracts the target’s local contrast information. Then, based on the target’s motion trajectory, we determine the temporal variation trajectory of the target’s contrast. Finally, by comparing the temporal fluctuation characteristics of the contrast between the target and background false positives, the network can effectively distinguish between the target and background, thereby suppressing false positives. The experimental results show that the visual network performs excellently in terms of detection rate and precision, with an average detection rate of 0.81 and an average precision as high as 0.0968, which are significantly better than those of other comparative methods. This indicates that it has a significant advantage in suppressing false alarms and identifying small targets in complex dynamic environments.