# An Accelerating Method of YOLOv7 Based on Lightweight Network Architecture
## 1 Introduction  
Optical imaging detection technology is widely used in various American weapon systems, such as missile defense systems, early warning detection systems, ground-based midcourse defense systems with kinetic energy interceptors, the THAAD weapon system, air defense missiles, and air-to-air missiles. Object detection algorithms process imaging data to determine whether a target is present within the detection field and its position, making it one of the key technologies in optical imaging detection systems [1, 2, 3]. Optical imaging detection technology has become an integral component in the development of various advanced weapon systems, especially in military applications. In the United States, optical imaging plays a critical role in missile defense systems, early warning detection systems, and ground-based midcourse defense systems with kinetic energy interceptors. Additionally, it is a key technology in the THAAD (Terminal High-Altitude Area Defense) system, air defense missiles, and air-to-air missiles. These weapon systems rely heavily on object detection algorithms, which process imaging data to determine the presence and position of targets within a given detection field. As such, optical imaging detection technology forms the backbone of many high-performance guidance systems, ensuring precise and reliable identification of threats in dynamic environments. The increasing complexity of modern battlefield environments, however, has created new challenges for optical terminal guidance systems, particularly those used in precision-guided weapons. The complex environment refers to the confluence of variouselectromagnetic activities, natural phenomena, and multi-target scenarios that can disrupt or degrade the ability of optical imaging systems to accurately detect and track targets. These factors are as follows: (1) Electromagnetic Environmental Elements: Electromagnetic interference can degrade the signal quality and increase the difficulty of target identification. (2) Natural Environmental Elements: Adverse weather conditions such as fog, rain, or dust storms can obscure the imaging sensor's view, negatively impacting detection accuracy. (3) Multi-target Environmental Elements: The presence of multiple targets within a scene can create confusion, leading to misidentification or misclassification. These environmental factors place significant demands on optical terminal guidance systems and impact their ability to track and engage targets effectively. This, in turn, influences the overall combat effectiveness of optical imaging-based precision-guided systems [4]. Traditional object detection and recognition methods were designed to address some of these challenges. Typically, these methods follow a multi-stage process: generating candidate regions, extracting features, classifying regions, and performing post-processing to refine detection results. However, these traditional approaches are limited by several factors, particularly their reliance on manually designed low-level visual features. These features are often handcrafted based on empirical knowledge and are typically limited to specific categories, rendering them less versatile in handling complex object transformations, occlusions, or variations across different scenarios. The lack of deep semantic understanding and the inability to generalize across diverse environments significantly hinder the performance of traditional models. The introduction of convolutional neural networks (CNNs) has revolutionized the field of object detection by enabling models to learn hierarchical feature representations directly from raw input data. CNN-based approaches have significantly outperformed traditional methods by eliminating the need for handcrafted features and providing more accurate and robust detection capabilities. Among these CNN-based models, the YOLO (You Only Look Once) series has emerged as one of the most popular and effective solutions for real-time object detection [5].