# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 1 Introduction
ACEP integrates the concept of Neural Architecture Search (NAS) by intelligently selecting and eliminating redundant parameters to comprehensively enhance neural network performance, rendering it more suitable for the unique requisites of remote sensing detection tasks. The enhancement strategy entails blending multiple ant colony algorithmic variations with population evolution concepts. Diverse ant colonies leverage their distinctive search strategies to explore independently and efficiently. Through natural selection competition among populations, robust populations gradually assimilate weaker ones, ensuring progressive optimization of the global search. The traditional NAS pruning method based on evolutionary algorithms typically eliminates individuals with poorer performance directly. In contrast, our method introduces the Ant Colony Optimization (ACO) [16] algorithm to record the importance of each weight. The ACO algorithm guides the subsequent search by accumulating pheromones, helping it avoid secondary weights, thereby improving search efficiency. In the ACO algorithm, exploration and exploitation are two key hyperparameters: exploration tends to choose paths that have not been sufficiently searched, while exploitation prefers paths with higher pheromone concentrations. We integrate evolutionary ideas into the ACO algorithm, specifically by allowing elite ants to prioritize exploitation to reinforce already discovered good paths, while ordinary ants prioritize exploration to discover new potential paths. When ordinary ants find better paths, they can be promoted to elite ants, further optimizing the search process. During the search process, considering the diversity of DETR neural network architectures, we have specifically designed sparse operation operators tailored to various structures to streamline the model's computational burden. Typically, remote sensing images contain limited feature information and are easily affected by image quality. In the initial stages of the search process, we employ a soft pruning method. This method helps us gradually evaluate the impact of each weight component on the neural network's performance. By doing so, we aim to maximize the retention of the model's representational capabilities. Additionally, remote sensing images generally feature small targets, and deep learning neural networks tend to extract more large-scale features and high-dimensional information in deeper layers. Taking into account the global feature utilization capabilities of Transformers [9], we introduce an environmental constraint framework in the search process. This framework uses Bayesian optimization to adaptively control the pruning ratios of different neural network layers, ensuring the preservation of essential image features. The main contributions of this study are as follows. 1. The development of an innovative algorithm employing ant colony evolutionary pruning for structured pruning, approaching neural network models with high sparsity and superior performance via an automated search process. 2. The proposal and integration of three efficient sparse operators tailored to the distinct module characteristics of the DETR neural network: channel sparse operators for convolutional neural networks, attention head sparse operators for self-attention mechanisms, and twin-channel sparse operators for coordinating entire residual structures. 3. The design of specialized environmental constraint tools to aptly allocate pruning ratios for neural network layers, catering to the demands of object features in remote sensing images. 4. Through extensive experiments, ACEP has showcased remarkable efficacy across various DETR-like models, furnishing a pragmatic avenue for model compression. The structure of this paper is as follows.