# An Accelerating Method of YOLOv7 Based on Lightweight Network Architecture
## 1 Introduction
YOLO combines high detection accuracy with impressive speed, making it ideal for real-time applications such as autonomous vehicles, surveillance systems, and military guidance systems. Since its inception in 2016, YOLO has undergone several iterations, from YOLOv1 to YOLOv11 [6; 7; 8; 9], with each version introducing improvements in speed and accuracy. YOLOv7, introduced in 2022, surpassed all other real-time object detectors by achieving an unprecedented balance between speed and accuracy. At 30 FPS (Frames Per Second) on a GPU V100, YOLOv7 achieved a peak accuracy of 56.8% AP (Average Precision), outperforming other real-time detectors in terms of both detection accuracy and processing speed. Despite its superior performance on high-performance GPUs, YOLOv7 faces challenges when deployed on resource-constrained platforms such as smartphones, drones, and embedded systems. These devices often lack the computational power and memory capacity needed to run large, complex models efficiently. To address this issue, various model compression and acceleration techniques have been developed to make deep learning models more suitable for deployment on these platforms [10; 11; 12; 13]. Common methods for accelerating neural networks include lightweight networks, weight quantization [14], neural network pruning [15], and knowledge distillation [16]. Weight quantization is a widely used technique in model compression, where the precision of the model's weights is reduced to lower-bit representations [17; 18]. This approach significantly reduces storage requirements and accelerates computation, but it often comes at the cost of reduced accuracy. This is particularly problematic for object detection models, where small errors in weight quantization can result in large deviationsin the predicted bounding box coordinates, affecting the overall detection quality. Binary and ternary quantization have been explored as means to further reduce the model size, but these approaches often introduce substantial accuracy losses, especially in the detection of small or occluded objects [19]. Neural network pruning, another common technique, involves removing less important weights or entire neurons from the model to reduce its size. While pruning can effectively reduce model's complexity, it also poses challenges when it comes to maintaining the model's performance, particularly when large portions of the network are removed. [PERSON]'s pruning method [20], which combines pruning with weight sharing and Huffman encoding, has been proposed to address this challenge by further compressing the model while attempting to retain its predictive power [21; 22; 23]. However, as the network grows more complex, the implementation of such techniques becomes increasingly difficult. There are also studies on early exit for acceleration [24; 25]. In recent years, lightweight networks have emerged as a promising solution for model acceleration, especially on mobile and embedded devices. Lightweight networks such as MobileNet [14], SqueezeNet [15], and ShuffleNet [16; 26] have been specifically designed to reduce the number of parameters and computational cost without sacrificing model accuracy. These networks utilize novel architectural techniques such as depthwise separable convolutions, which significantly reduce the number of operations required for convolutional layers while maintaining a high performance. Depthwise separable convolutions decompose the standard convolution operation into two steps: a depthwise convolution followed by a pointwise convolution, which reduces the number of computations and memory usage. This makes lightweight networks particularly effective for real-time object detection on devices with limited resources. In this paper, we explore the application of lightweight network principles to the YOLOv7 object detection model to improve its performance on resource-constrained hardware.