Lobula Giant Movement Detectors (LGMD1 and LGMD2), neurons located in the locust’s optic lobe, are specialized in detecting approaching objects (looming perception) and have been widely modeled for integration into mobile robots. In bio-inspired robotic implementations of LGMD, inhibitory processes are crucial, as they help maintain selective responses to looming stimuli, enabling reliable collision avoidance. However, current robotic implementations of LGMD models often struggle with nearby translating movements, frequently generating false-positive collision alerts. Recent biological studies have identified trans-medulla afferent (TmA) neurons within the LGMD dendritic region, which may act as a form of self-inhibition (SI). These neurons rapidly suppress intermediate neuronal activities in situ within the LGMD structure, effectively complementing lateral inhibition (LI). Together, SI and LI enhance the specificity of looming responses, reducing interference from translating motions. Despite their biological significance, these mechanisms have yet to be effectively modeled and tested within artificial robotic vision systems. In response, this study introduces a biomimetic visual neural model that incorporates SI and coordinates it with LI during looming perception. The proposed neural computation explicitly activates SI during initial looming events and during translating movements by leveraging spatial correlations within segmented, localized image areas, defined as the local visual field (LVF). This innovative model has been integrated into a bio-inspired micro-robot, named Colias, serving as its sole collision detection mechanism. Both offline evaluations and real-world robotic tests demonstrate the efficacy of the biomimetic model in distinguishing looming from translating motions. Consequently, the robot exhibits significantly enhanced collision detection selectivity, closely resembling the capabilities observed in biological organisms.