Research on Improved Multi-Target Tracking Detection of YOLOv8s in Autonomous Driving Scenario
In order to solve the problem that the existing autonomous driving models do not have high recognition accuracy for small samples and overlapping samples,a lightweight object detection model based on improved YOLOv8s is proposed.A C2f-Faster module is designed by using multi-scale feature extraction to replace the C2f module of YOLOv8s backbone network and neck network.The Inner-MPDIoU loss function is proposed by fusing the Inner-IoU and MPDIoU-based loss function based on the Minimum Point Distance based Intersection over Union(MPDIoU).The results of the comparative test and ablation experiment of the model show that when the cross-union ratio is 0.5,the average accuracy of the model(mAP50)is increased by 3.5 percentage points,the accuracy reaches 95.2%,and the number of parameters decreases by 25%.Through the visual analysis of the data,the effectiveness of the improved model for complex scenarios is further verified.
Autonomous drivingDeep learningObject detectionYOLOv8sLoss function
NETL
NSTL
万方数据
