## 1 Introduction  
Video surveillance has become very important in nowadays. An enormous number of surveillance cameras are installed at public places, especially in railway stations and airports. These surveillance cameras play the role of not only surveillance of infrastructural property and public safety, but also sensor for data collection. The flight operation process is mainly divided into two stages, the flight stage and the ground service stage. During the flight phase, the flight is dispatched by air traffic control to complete the transport of passengers under the service of the crew members. During the ground service stage, the flight needs to complete various pre-flight preparations, which can be divided into five processes including passengers, luggage, cargo, fueling, and cleaning [1]. These operations will be completed on the apron. Consequently, the normal advancement of airport apron operations is very important for the normal operation of the flight, and its various operating data are of great significance for flight security and airport operation analysis. However, for airport apron operations, it is difficult to collect various operational data using traditional sensors. At present, the main method adopted is to use humans to monitor the operation monitoring video to realize the guarantee of the operation process. Nonetheless, the human processing will inevitably lead to a series of problems such as untimely response and human physiological fatigue. With the development of artificial intelligence technology, some researchers try to use the object detection technology to create a more intelligent video surveillance system [2]. Compared with manual video surveillance, intelligent video surveillance cannot only process faster and better, but also save a lot of unnecessary labor costs. However, it has higher requirements for computing resources and performs poorly on embedded devices with weak computing power. From the perspective of security assurance and intelligent video analysis, it is necessary to conduct real-time detection of surveillance video on the apron. However, taking the current typical one-stage object detection model YOLOv5s [3] as an example, using a standard 640 \(\times\) 640 input, it can detect about three to four frames per second on the Jetson Nano and about two frames per second on the Raspberry Pi 4B, which is far from real-time detection. The use of GPU-equipped cloud servers can certainly solve the real-time problem, but it will also put a lot of pressure on bandwidth, and its cost and power consumption cannot be ignored, which greatly limits its practical value. Some researchers use methods such as model pruning and model quantization to reduce the computational load and memory usage of the detection model, so that it can be better deployed on edge embedded devices. [PERSON] [4] proposed a light YOLOv4 pruned using the scientific control-based neural network pruning algorithm for citrus detection under orchard environment. [PERSON] [5] proposed a low bit-based convolutional neural network, which uses 1-bit weights to reduce the kernel parameter size and 8-bit activations to increase the speed for one-class object detection. However, the pruned and the quantized models suffer from decreased detection accuracy and increased deployment difficulty. Meanwhile, the models also face to the challenges of insufficient generalization ability when migrated to new scenarios, due to so many monitoring scenarios on the apron. The special equipment is required to speed up the detection of the quantized model. In recent years, model lightweight research in the field of image classification has developed rapidly, and a series of efficient image classification networks such as MobileNet [6; 7; 8], ShuffleNet [9; 10], and EfficientNet [11] have emerged. Inspired by this, we build a light object detection model for the edge devices by combining model lightweight techniques with object detection tasks and propose a cloud sampling correction mechanism to hold the accuracy of the detection results.