Based on the Optimization and Performance Evaluation of YOLOv8 Object Detection Model with Multi-backbone Network Fusion

The paper aims to enhance the recognition accuracy of the YOLOv8 object detection model in complex scenarios. To achieve this goal, various high-performance backbone networks, including ResNet50, DenseNet169, ConvNeXt, and EfficientNetv2, are integrated with YOLOv8 to construct four novel detection models: YOLOv8-ResNet50, YOLOv8-DenseNet169, YOLOv8-ConvNeXt, and YOLOv8-EfficientNetv2. These models combine the unique characteristics of each backbone network, aiming to further improve detection accuracy while maintaining YOLOv8's real-time performance. Rigorous experimental validation is conducted on a self-constructed leaf mustard dataset. The experimental results demonstrate that YOLOv8-EfficientNetv2 performs the best among these models, achieving a high accuracy of 95.2% in mAP50 and 85.3% in mAP50:95. Compared with the original YOLOv8, YOLOv8-EfficientNetv2 exhibits improvements of 0.87% and 1.6% in mAP50 and mAP50:95, respectively, significantly enhancing the accuracy of object detection. This research provides novel ideas and methods for the application of YOLO series models in complex scenarios, laying a solid foundation for future object detection research.