LKF-YOLO: target detection model of lightweight road traffic based on YOLOv8

With the rapid development of deep learning, road traffic target detection has become increasingly popular in intelligent video surveillance and vehicle-assisted driving. However, traditional road traffic target detection algorithms have limitations in terms of generalization ability and recognition rate. To address this issue, this paper proposes a road traffic target detection model named LKF-YOLO. To begin with, we propose a novel and efficient down-sampling module to replace the existing down-sampling structure in YOLOv8. Furthermore, by incorporating KaernelWareHouse, we have successfully mitigated the substantial computational expense of C2f modules in YOLOv8 while enhancing the model's representative capacity. Finally, we have incorporated Focal Modulation Networks to replace SPPF, thereby augmenting the model's capacity to assimilate contextual information. The experimental results demonstrate that the mAP50 index of LKF-YOLO has increased by 2.3%, the recall rate has increased by 4.7%, and both parameters and GFLOPs have decreased. These findings indicate that our improvement strategy effectively enhances the detection accuracy of the model and reduces its size. This study provides an efficient and accurate solution for road traffic target detection, with broad application prospects.