Attention-Driven LPLC2 Neural Ensemble Model for Multi-Target Looming Detection and Localization

• URL: http://arxiv.org/abs/2504.04477v1
• Date: Sun, 06 Apr 2025 13:07:57 GMT
• Title: Attention-Driven LPLC2 Neural Ensemble Model for Multi-Target Looming Detection and Localization
• Authors: Renyuan Liu, Qinbing Fu,
• Abstract summary: Lobula plate/lobula columnar, type 2 (LPLC2) visual projection neurons in the fly's visual system possess highly looming-selective properties.<n>Existing models of LPLC2 neurons either concentrate on individual cells to detect centroid-focused expansion or utilize population-voting strategies to obtain global collision information.<n>We propose a numerical model for LPLC2 populations, leveraging a bottom-up attention mechanism driven by motion-sensitive neural pathways to generate attention fields.
• Score: 2.3204178451683264
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Lobula plate/lobula columnar, type 2 (LPLC2) visual projection neurons in the fly's visual system possess highly looming-selective properties, making them ideal for developing artificial collision detection systems. The four dendritic branches of individual LPLC2 neurons, each tuned to specific directional motion, enhance the robustness of looming detection by utilizing radial motion opponency. Existing models of LPLC2 neurons either concentrate on individual cells to detect centroid-focused expansion or utilize population-voting strategies to obtain global collision information. However, their potential for addressing multi-target collision scenarios remains largely untapped. In this study, we propose a numerical model for LPLC2 populations, leveraging a bottom-up attention mechanism driven by motion-sensitive neural pathways to generate attention fields (AFs). This integration of AFs with highly nonlinear LPLC2 responses enables precise and continuous detection of multiple looming objects emanating from any region of the visual field. We began by conducting comparative experiments to evaluate the proposed model against two related models, highlighting its unique characteristics. Next, we tested its ability to detect multiple targets in dynamic natural scenarios. Finally, we validated the model using real-world video data collected by aerial robots. Experimental results demonstrate that the proposed model excels in detecting, distinguishing, and tracking multiple looming targets with remarkable speed and accuracy. This advanced ability to detect and localize looming objects, especially in complex and dynamic environments, holds great promise for overcoming collision-detection challenges in mobile intelligent machines.

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