Research on Real-time Object Detection Method Based on Improved YOLOv4
To enhance the accuracy and robustness of real-time object detection,this paper optimizes the YOLOv4 algorithm by employing enhanced feature fusion technology,network architecture technology,loss function technology,and other strategies.The results demonstrate that the improved YOLOv4 algorithm exhibits excellent performance in detecting small objects in diverse environments,showcasing its practicality and stability,and laying a solid foundation for its widespread application.
real-time object detectionYOLOv4feature fusionGIoU loss function
NETL
NSTL
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