A bionic vision method for extracting motion information of small-target in cotton field backgrounds

During the operation of long-staple cotton picking robots, the slight wobbling of cotton bolls caused by dynamic uncertainties can result in the loss of shape and texture information within a few pixels. Low signal-to-noise ratio, background clutter, and occlusion issues further degrade the performance of traditional detection methods, increasing the probability of missed detections and inaccurate positioning. This study proposes a neural visual pathway model based on the drosophila visual system for precise localization and dynamic tracking of cotton bolls. The model simulates the sensitivity of the drosophila neural visual pathway by responding to weak motions of small targets in cluttered backgrounds, and introduces a direction-selective inhibition algorithm to reduce background interference. Experiments show that the model performs stably in detecting small targets with a wide range of speeds and sizes, and can quickly and accurately extract motion direction and energy information.