## 2 Related Work
### YOLOv7-tiny Network Structure  
YOLO is an object detection model that changes the traditional approach to object detection. It applies a single CNN [30] to process the entire image, divides the image into grids, and makes predictions. These predictions include the class probabilities and bounding box coordinates for any object present within each grid cell. The method enables YOLO to efficiently and accurately detect objects in images in real time. YOLOv1 [9] was introduced in 2016 by [PERSON] et al. It is an end-to-end object recognition and detection method that predicts the probabilities of object categories in a complete image. In 2017, [PERSON] and team unveiled YOLOv2 [10], which improved on some shortcomings of YOLOv1 and significantly improved its accuracy and number of object detections. YOLOv2 adopted the network structure of DarkNet-19 [31] instead of the GoogLeNet [32] network structure of YOLOv1. The network structure had no fully connected layers, and there were five down samplings, all of which were convolution operations. The 1 \(\times\) 1 convolution operation saved the parameters. YOLOv3 [11], introduced by [PERSON] et al. in 2018, represented a significant advancement in the YOLO series. Compared with its predecessors, YOLOv3 placed a primary emphasis on network architecture improvements, one of which was the adoption of the DarkNet-53 network structure, which was deeper and more complex than the earlier structures. This structural enhancement simultaneously improved the speed and accuracy of object detection. YOLOv3 introduced the idea of ResNet [33], stacking more layers for feature extraction and using spatial pyramid pooling networks [34] to achieve multi-size input and same-size output. In April 2020, [PERSON] et al. [12] improved YOLOv3 and proposed the more powerful YOLOv4 algorithm. The network structure adopted was Cross Stage Partial Networks 53 (CSP DarkNet-53). In YOLOv4, a spatial pyramid pooling network (SPP-Net) was added to enable the model to adapt to inputs of different sizes. A Path Aggregation Network (PANet) [35] was incorporated to fully exploit feature fusion. In June 2020, [PERSON] et al. launched YOLOv5 [13; 14; 15]. The Leaky ReLU activation function, commonly used in neural network architectures, was applied in the middle or hidden layers, and the final detection-layer activation function was sigmoid. In 2020, [PERSON] et al. proposed the YOLOv7 [16; 17] algorithm. YOLOv7 continued optimization based on YOLOv5, and had obvious advantages in detection accuracy and speed. First, YOLOv7 incorporated a redesigned extended efficient aggregation network to enhance its feature extraction capabilities. The max-pooling convolution (MPConv) module was introduced for the down sampling operation. This module added a convolution operation based on pooling and realized down sampling through the dual operation of pooling and convolution, which reduced the loss of features. Improved SPP was the final part of the backbone network, and a set of convolution operations was integrated into multiple parallel pooling operations to avoid problems such as image distortion. In the neck network, the PANet structure was still used for network aggregation to ensure the effective integration of different feature layers. In the final prediction head network, REPConv [36] was used to adjust the number of channels. REPConv had different structures during the training and inference stages. Using the concept of reparameterization, the structure of REPConv was simplified in the inference stage without losing accuracy. Although the accuracy ofYOLOv7 has improved over the series, the network structure is too complex, the number of parameters too large, and the equipment performance requirements too high for it to be used for edge-terminal equipment. YOLOv7-tiny [18; 19] simplifies the structure based on YOLOv7, which is a network model designed for edge GPUs.