# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 1 Introduction  
Real-time object detection plays a crucial role in intelligent remote sensing systems, offering the advantage of all-weather capability. It has been widely utilized in applications such as meteorological observation, urban planning, environmental monitoring, and military surveillance, showing promising potential [1, 2, 3, 4]. The remote sensing images used in these applications are image data acquired by remote sensing technology, usually taken by sensors mounted on satellites, aircrafts, or drones. Remote sensing images often contain vast amounts of information, possess large dimensions, and are susceptible to natural conditions, posing challenges for object detection tasks. In recent years, with advancements in technology, deep learning techniques have exhibited exceptional performance in cross-scene and precise object detection [5, 6, 7, 8]. However, achieving enhanced detection performance often involves employing advanced methods characterized by large neural network structures and intricate computational operations. The integration of Transformer neural networks has augmented the complexity of object detection architectures, albeit exhibiting robust global feature-capturing capabilities through their self-attention mechanism, which is critical for remote sensing target detection. According to a survey [9], introducing Transformers into the remote sensingfield has also become a trend in the industry. Since 2020, the overall trend of the number of papers related to Transformers in the remote sensing field has been rapidly increasing. Within the realm of Transformer technology, particularly the Detection Transformer (DETR) [10] series of neural networks, a groundbreaking end-to-end object detection principle has emerged, fundamentally diverging from traditional anchor-based methods. DETR eradicates the reliance on predefined anchors for object classification and bounding box regression, augmenting feature map-capturing capabilities and transcending the constraints of convolutional neural networks. Despite showcasing superior detection performance, the complexity of DETR leads to parameter inflation and increased computational costs, hindering its widespread implementation in real-time applications. Research has shown that significantly reducing redundant parameters in deep learning models is viable and can substantially decrease computational costs [11; 12; 13]. Pruning and compression techniques have emerged as crucial research directions for optimizing DETR neural networks. Fine-grained pruning methodologies, which entail eliminating redundant neurons and connections, reduce model complexity while preserving or even enhancing detection performance. Additionally, compression techniques effectively reduce model storage requirements and hasten inference speeds, bolstering the efficiency of DETR neural networks. The endeavor to integrate cross-domain compression techniques holds paramount importance in unlocking the full potential of DETR neural networks. Nevertheless, existing compression techniques have predominantly concentrated on refining convolutional neural networks (CNNs) or Transformer single structures [14; 15], with few tailormade for complex hybrid architectures like DETR. Consequently, the development of efficient compression methods for DETR not only bolsters its computational efficiency and real-time performance across diverse application scenarios but also propels the utilization of the entire Transformer domain in remote sensing object detection endeavors. These initiatives are poised to better equip us for addressing the challenges associated with processing large-scale visual data in the future and facilitating efficient remote sensing object detection in resource-constrained environments. To construct a more efficient and lightweight DETR neural network, this study proposes an innovative neural network pruning methodology named ant colony evolutionary pruning (ACEP).