Modeling Drosophila vision neural pathways to detect weak moving targets from cluttered backgrounds. (April 2022) - Shen, Keyong; Yang, Yang; Liang, Yuying; Xu, Lizhong
Abstract: In target detection and recognition, low intensity and small volume characterize weak targets and pose serious challenges to their detection. Cluttered backgrounds pose another difficulty for target detection by generating a large amount of interfering noise. Detecting weak moving targets from cluttered backgrounds remains a challenging task; however, this topic has been extensively investigated for some time. The vision of Drosophila flies provides an excellent paradigm to address this issue because it can accurately and efficiently detect weak moving targets during high-speed flight. This type of biological behavior provides valuable inspiration for weak target detection methods; however, it has not been fully analyzed in the relevant literature thus far. In this study, we propose a neural vision pathway model based on the visual system of Drosophila to functionally simulate neural activation for the detection of weak moving targets. Specifically, we mathematically modeled a weak target motion detector to generate a group of responses to small motions in cluttered backgrounds. We then fused these responses to model the consequent sensitivity of the Drosophila neural vision pathways. The proposed model functionally interprets the advantages of the neural vision architecture of the species to perform the target-detection task. Experimental results demonstrated the effectiveness of the proposed bionic model in detecting weak moving targets embedded in cluttered backgrounds. Moreover, we systematically analyzed the sensitivity preferences of the model for different target sizes. These findings are qualitatively and quantitatively presented to demonstrate the neurobiological advantages of the neural architecture of Drosophila flies on a conventional machine learning challenge.