AFL-YOLO:基于YOLOv8改进的小目标检测算法OA

For the problems such as low accuracy,missed detections,and false positives in small object detection tasks,an algorithm named AFL-YOLO based on YOLOv8n is proposed.The algorithm is characterized by several enhancements introduced to improve its performance.Firstly,the Shape-IoU loss function is incorporated,whereby the dynamic feature adaptation mechanism allows for a more precise focus on the shape and scale of bounding boxes,leading to improved regression of these boxes.Secondly,space-to-depth convolution(SPD-Conv)is embedded within the backbone network,mitigating the problem of fine-grain information loss.Thirdly,the receptive-field attention convolution(RFAConv),along with the construction of the C2F_RFAConv module,is introduced to strengthen the model's capability in learning global contextual feature information.Finally,the detection layer undergoes optimization to enhance detection accuracy while reducing the model's parameter count.Experimental results demonstrate that,compared with YOLOv8n,AFL-YOLO achieves improvements of 5.3%,3.6%,4.4%,and 4%in mAP@0.5,mAP@0.5:0.95,precision,and recall,respectively,on the VisDrone2019 dataset,with a concurrent reduction of 20.5%in parameters.Additionally,generalization comparison experiments on the TinyPerson dataset prove that the proposed AFL-YOL algorithm effectively enhances the accuracy of detecting small objects while ensuring the lightweight nature of the model.