EdgeTrim-YOLO: Improved Trim YOLO Framework Tailored for Deployment on Edge Devices

A trim YOLO framework tailored for deployment on edge devices, named EdgeTrim-YOLO, is proposed in this study. Given the limited computing resources of edge devices, traditional YOLO frameworks often fall short of meeting the requirements for real-time performance and model efficacy. To address this issue, we conducted deep optimization and customization of the YOLO framework, introducing GhostConv, DFC Attention, and structural re-parameterization training strategies into the native backbone. These modifications significantly reduced the model's complexity and computational burden while maintaining high detection accuracy on the COCO dataset. Experimental results demonstrate that, compared to the original YOLO framework, the proposed trim YOLO framework achieved an increase in inference speed by 22.4 % on CPU (ARM), 8.2% on GPU, and 19.3% on NPU, respectively, while maintaining comparable detection performance to YOLO v5s. This provides an efficient and feasible solution for real-time object detection applications on edge devices.