# Lightweight Object Detector Based on Images Captured Using Unmanned Aerial Vehicle
## 1 Introduction  
The technology of unmanned aerial vehicles (UAVs), characterized by low cost and high efficiency, has substantially widened the scope and boundaries of UAV applications. As UAV technology matures and gains widespread adoption, its fields of application are continually expanding and diversifying. Aerial photography, as one of the earliest and most sophisticated applications of UAVs, continues to lead the way in industrial innovation and development, offering unparalleled visual experiences and data support in sectors such as agricultural production [1], environmental protection [2], safety and security [3], and rural environments [4]. The application of UAVs in petrochemical and power grid inspection is becoming increasingly widespread [5], as the efficient, convenient, and flexible characteristics of UAVs have significantly improved the quality and efficiency of inspection tasks. In the petrochemical field, UAVs are capable of flying over complex terrain and reaching areas that are difficult to access, carrying out high-definition filming and the infrared scanning ofkey facilities such as oil pipelines and storage tanks. In power grid inspections, faced with vast transmission lines and dense substations, UAVs can swiftly identify potential faults, such as line aging and insulator breakage. Against the backdrop of rapid advancements in UAV technology, UAV aerial photography target recognition models, as key technologies, are gradually penetrating into various fields, including environmental monitoring, agricultural management, emergency rescue, and film and television production. [PERSON] et al. [6] lightened a model by introducing the Fasternet structure. [PERSON] et al. [7] introduced an SCConv module into C2f to design an improved C2f-SCConv module in order to reduce feature redundancy in spatial and channel dimensions and reduce the computational load of the model. [PERSON] et al. [8] improved the network by creating a module with cross-stage local-connection feature extraction and feature fusion capabilities. [PERSON] et al. [9] reconstructed the Yolov8 backbone network by introducing Deformable Convolutionv4 and Swin Transformer encoder structures to enhance its feature transfer and extraction capabilities and improve its sensitivity to small objects. [PERSON] et al. [10] combined an existing fast single-frame detection method with the spatio-temporal relationships of video sequences to design an efficient lightweight object detection model; however, the inference speed was only around 20 fps. [PERSON] et al. [11] introduced a BiFormer attention mechanism module in the backbone and head networks to enhance the algorithm's ability to detect small objects. Although most studies have focused on methods for improving the recognition accuracy of a model through optimizing the algorithm's structure and its feature extraction and fusion capabilities, they tend to ignore the uniqueness and environmental constraints of UAV applications, which introduces viability and deployment efficiency problems for relevant models. Therefore, real-time and low-latency object detection algorithms are crucial. Traditional high-performance models are highly accurate, but also have high computational complexity and long processing times, making it difficult to meet to rapid response demands of UAVs [12]. To achieve this goal, it is imperative to explore more efficient algorithm designs, such as adopting lightweight network structures, optimizing the size and number of convolutional kernels, and introducing quantization and pruning techniques to reduce the number of model parameters and computational loads. This is expected to enable significant enhancements in processing speed and reduced latency while maintaining a certain level of accuracy. In addition to optimization at the technical level, the robustness and adaptability of UAV-based [13] aerial object recognition models in practical applications should also be considered.