Brain-Inspired Robust Detection Method for Small UAV Targets in Degraded Electro-Optical Images

‍ ‍ In complex environments， various types of noise interference， including sensor noise， electronic interference， and weather conditions， can significantly degrade the accuracy and robustness of Unmanned Aerial Vehicle （UAV） target detection. These interferences not only deteriorate image quality but also lead to the loss or distortion of target features， thereby negatively impacting the performance of detection models. This problem is particularly pronounced in UAV detection tasks， where targets are often small and the background is highly complex， making detection even more challenging under noise interference. To address this issue， this study proposes a novel target detection model inspired by the multiscale orientation-selective receptive fields of the primary visual cortex （V1）， termed MORF-YOLO. This model leverages the characteristics of the human visual system， employing anisotropic Gaussian kernels to simulate the receptive field mechanisms of V1 neurons， thereby extracting multi-scale and orientation-selective features from images. Integrating a V1 visual information guidance module （MORF module） into the YOLO object detection framework， MORF-YOLO enhances the representation ability of low-level features， significantly improving the adaptability and robustness of the model to noise interference. To validate the effectiveness of the proposed model， we constructed a dataset with varying noise levels based on the AntiUAV2021 dataset. We compared the performance of MORF-YOLO with those of several state-of-the-art object detection methods， including YOLOv5， DiffusionDet， and DETR. Experimental results demonstrate that MORF-YOLO achieves superior detection accuracy under both noise-free and different noise-intensity conditions. Specifically， in strong Gaussian noise scenarios （with a noise variance of 0.18）， MORF-YOLO exhibits a 5%-30% improvement in detection precision （mAP@0.5） compared with other methods. Moreover， under low and medium noise conditions， MORF-YOLO shows significantly higher precision and recall rates than the compared methods. Additionally， in blurred noise and salt pepper noise scenarios， MORF-YOLO demonstrates stronger robustness， effectively reducing false detection rates and enhancing detection stability.