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Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction module (SDCN). Moreover, the model undergoes pruning to eliminate redundant channels. Finally, the non-maximum suppression algorithm of intersection-union ratio based on minimum point distance (MPDIOU_NMS) algorithm has been integrated to eliminate redundant detection boxes, and a comprehensive validation has been conducted using the infrared aerial dataset and the Visdrone2019 dataset. The comprehensive experimental results demonstrate that when the number of parameters and floating-point operations is reduced by 30% and 20%, respectively, there is a 1.2% increase in mean average precision at a threshold of 0.5 (mAP(0.5)) and a 4.8% increase in mAP(0.5:0.95) on the infrared dataset. Finally, the mAP on the Visdrone2019 dataset has experienced an average increase of 12.4%. The accuracy and recall rates have seen respective increases of 9.2% and 3.6%.

Unmanned Aerial Vehicles General Aerial Person-Vehicle Recognition Based on Improved YOLOv8s Algorithm