An Accelerating Method of YOLOv7 Based on Lightweight Network Architecture

Object detection is a key technology in optical imaging detection systems. To address the issue of slow object detection model performance on mobile platforms, a lightweight network can be used as the backbone of the object detector, reducing the model’s parameters and increasing the inference speed. However, while using a lightweight network directly as the backbone improves inference speed, it significantly reduces detection accuracy. This paper proposes enhancing the backbone network by adding depthwise separable convolutional layers, which improves the receptive field of the model, enabling better detection performance in complex environments. The experimental results show that, compared to YOLOv7, the improved model reduces detection accuracy by 3.84%, while achieving a detection speed that is 3.77 times faster than the original model. This method significantly enhances the model’s suitability for real-time applications on resource-constrained devices, offering an effective solution for faster object detection without sacrificing critical performance in practical scenarios.