Amari's Dynamic Neural Field (DNF) framework provides a brain-inspired approach to modeling the average activation of neuronal groups. Leveraging a single field, DNF has become a promising foundation for low-energy looming perception module in robotic applications. However, the previous DNF methods face significant challenges in detecting incoherent or inconsistent looming features--conditions commonly encountered in real-world scenarios, such as collision detection in rainy weather. Insights from the visual systems of fruit flies and locusts reveal encoding ON/OFF visual contrast plays a critical role in enhancing looming selectivity. Additionally, lateral excitation mechanism potentially refines the responses of loom-sensitive neurons to both coherent and incoherent stimuli. Together, these offer valuable guidance for improving looming perception models. Building on these biological evidence, we extend the previous single-field DNF framework by incorporating the modeling of ON/OFF visual contrast, each governed by a dedicated DNF. Lateral excitation within each ON/OFF-contrast field is formulated using a normalized Gaussian kernel, and their outputs are integrated in the Summation field to generate collision alerts. Experimental evaluations show that the proposed model effectively addresses incoherent looming detection challenges and significantly outperforms state-of-the-art locust-inspired models. It demonstrates robust performance across diverse stimuli, including synthetic rain effects, underscoring its potential for reliable looming perception in complex, noisy environments with inconsistent visual cues.