Zheng Yi, Wang Yusi, Wu Guangrong, Li Haiyang, Peng Jigen
School of Mathematics and Information Science, Guangzhou University, Guangzhou, China.
Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China.
Front Neurosci. 2023 Sep 5;17:1247227. doi: 10.3389/fnins.2023.1247227. eCollection 2023.
Lobular giant motion detector (LGMD) neurons, renowned for their distinctive response to looming stimuli, inspire the development of visual neural network models for collision prediction. However, the existing LGMD-based models could not yet incorporate the invaluable feature of depth distance and still suffer from the following two primary drawbacks. Firstly, they struggle to effectively distinguish the three fundamental motion patterns of approaching, receding, and translating, in contrast to the natural abilities of LGMD neurons. Secondly, due to their reliance on a general determination process employing an activation function and fixed threshold for output, these models exhibit dramatic fluctuations in prediction effectiveness across different scenarios.
To address these issues, we propose a novel LGMD-based model with a binocular structure (Bi-LGMD). The depth distance of the moving object is extracted by calculating the binocular disparity facilitating a clear differentiation of the motion patterns, after obtaining the moving object's contour through the basic components of the LGMD network. In addition, we introduce a self-adaptive warning depth-distance, enhancing the model's robustness in various motion scenarios.
The effectiveness of the proposed model is verified using computer-simulated and real-world videos.
Furthermore, the experimental results demonstrate that the proposed model is robust to contrast and noise.
小叶巨运动检测器（LGMD）神经元以其对逼近刺激的独特反应而闻名，激发了用于碰撞预测的视觉神经网络模型的发展。然而，现有的基于LGMD的模型尚未纳入深度距离这一宝贵特征，并且仍然存在以下两个主要缺点。首先，与LGMD神经元的自然能力相比，它们难以有效区分逼近、后退和平移这三种基本运动模式。其次，由于它们依赖于使用激活函数和固定输出阈值的一般判定过程，这些模型在不同场景下的预测有效性表现出剧烈波动。
为了解决这些问题，我们提出了一种具有双目结构的新型基于LGMD的模型（Bi-LGMD）。在通过LGMD网络的基本组件获得运动物体的轮廓后，通过计算双目视差来提取运动物体的深度距离，从而便于清晰区分运动模式。此外，我们引入了自适应警告深度距离，增强了模型在各种运动场景中的鲁棒性。
使用计算机模拟和真实世界视频验证了所提出模型的有效性。
此外，实验结果表明所提出的模型对对比度和噪声具有鲁棒性。
