Research on YOLOv3 Method Based on SE Module

Object detection is a crucial and challenging task in computer vision. With advancements in deep learning technology, YOLOv3 has become a widely adopted and efficient object detection algorithm. However, YOLOv3 encounters challenges when managing complex scenes and detecting small objects. To tackle these challenges, this research introduces an enhanced YOLOv3 architecture incorporating the Squeeze-and-Excitation (SE) module to improve feature representation capabilities. The SE module captures the interdependencies between channels and dynamically adjusts their feature responses, enhancing the models representation capability. By integrating the SE module into YOLOv3, this study seeks to substantially improve the models effectiveness in complex scenes and small object detection. Experimental results indicate that the enhanced YOLOv3 surpasses the original model on the COCO2017 dataset, validating the effectiveness of this method. Additionally, the improved architecture further enhances detection accuracy and robustness while maintaining efficient detection speed. The contribution of this study lies in proposing an effective feature enhancement method, introducing innovative concepts and techniques for object detection.