基于YOLOv8手机部署目标检测算法改进

Object detection technology is an important aspect of image processing, but in some special situations, it is difficult to perform real-time object detection. As a widely used and portable device, mobile phones have gradually become one of the necessities of daily life. Therefore, applying object detection technology to mobile phones can achieve real-time object detection. This article improves the model based on YOLOv8, introducing attention mechanism in the C2f module to enhance feature extraction of images and achieve the goal of improving detection accuracy. By introducing CBAM attention mechanism after the BottleNeck module in the C2f module, more feature information can be obtained during the processing. Experimental results have shown that the introduction of attention mechanism effectively improves detection accuracy, and real-time object detection on mobile deployment can obtain most of the target information, achieving good detection results.