## 3 Proposed Methods  
### Lightweight Detection Model Edge-YOLO  
As shown in Figure 2, the proposed lightweight detection model is called Edge-YOLO which is based on the combination of YOLOv5, ShuffleNetv2, and coord attention [2]. It is designed for the edge embedded device to detect objects from airport apron surveillance.  
The proposed Edge-YOLO is built by simplifying the structure of YOLOv5 and the new network architecture reduces the amount of parameters by more than 90%. YOLOv5 [3] is an end-to-end method for object detection based on nonregional candidates, and it is the current state-of-the-art method in the field of one-stage object detection. Its network structure is mainly divided into two parts: backbone (feature extraction network) and head (detection head). There are four versions of YOLOv5, namely YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x. The model size and accuracy of the four versions increase in turn, and the channel and layer control factors are used to select the appropriate size model according to the application scenario. In order to obtain a model that is easier to apply to resource-limited embedded devices, we choose YOLOv5s with the best real-time performance as the benchmark for optimization. Its network structure is mainly divided into two parts: backbone (feature extraction network) and head (detection head). In order to improve its detection speed, we chose to simplify the structure of both parts.  
For backbone, we choose to use a light-weight image classification network for feature extraction. After comparing some light-weight image classification networks, we choose to use shuffleNetv2 [10] with the least amount of parameters and the best real-time performance for feature extraction. ShuffleNetv2 [10] is an image classification network designed for mobile devices and replaces the original backbone. This structure greatly reduces the computation of convolution operations by using group point wise convolutions. Moreover, a mechanism for information exchange between groups called ChannelShuffle, is used to achieve information fusion between groups. Compared with the original YOLOv5s feature extraction network, ShuffleNetv2 has better speed performance on edge devices. Table 1 shows the parameters and calculation complexity comparison of YOLOv5s-backbone and ShuffleNetV2 [10].  
Figure 2: Net structure of Edge-YOLO.  
For the detection head part, the number of channels is cropped, since the number of categories to be detected in the airport apron scene is much less than that in the general scene. There are no more than 20 categories in the airport apron scene, while the detected categories are 80 in the COCO dataset. Only a quarter of channels is kept in order to get the highest cost-effectiveness in accuracy loss and compression ratio. Table 2 shows the parameters and calculation complexity comparison of YOLOv5s-head and Edge-YOLO-head.  
The reduction of the computational complexity of the feature extraction network may cause the weakening of the feature extraction capability. The coord attention [27] module is used to the extracted features, in order to make full use of the extracted features. This module is able to make the model pay attention to the parts that have a more significant impact on the final result. As shown in Figure 2, the coord attention [27] module decomposes channel attention into two 1D feature encoding processes. Since these two processes aggregate features along different directions, the long-range dependencies can be captured along one spatial direction, while precise location information can be preserved along the other. Then, the generated feature maps are separately encoded to form a pair of orientation-aware and position-sensitive feature maps, which is able to enhance the representation of objects of interest according to the complementary feature maps.