## 3 Our Proposed Object Detection Algorithm Combined with Varyblock
### YOLO-Tiny Integrated VaryBlock  
YOLO has a detection speed of 45 images per second. The speed advantage makes it the leader of end-to-end object detection networks. YOLO's ideas different from other object detection frameworks are as follows: first, it uses the convolution neural network to extract a huge number of features of images which are not overlapping, and therefore, the fixed-size feature map is obtained. If the coordinates of the center point of an object's ground truth fall on a grid, it divides the input image into S \(\times\) S grids that are in charge of detecting and anticipating the object. It is the advantage of acceptable and real-time detection speed, and the detection accuracy and boundary recall rate can meet the requirements. Although it loses a certain accuracy, it greatly improves the detection speed. YOLO-Tiny series methods are designed based on a complete YOLO series network. They are realized by suppressing the network of the complete YOLO series network. YOLOv3-Tiny is an outstanding network of the YOLO-Tiny series, which is very fast and can even perform object detection tasks on mobile phones. The YOLOv3-Tiny network has only 24 layers, which is 86 layers less than that of YOLOv3. The convolution layer with a step size of 2 in YOLOv3 is replaced by the pooling layer in YOLOv3-Tiny. In addition, YOLOv3-Tiny has only two detection scales, which are 13 \(\times\) 13 and 26 \(\times\) 26, each of which should have three boundary boxes with different scales. It uses a separate CNN framework to attain end-to-end object detection. Based on the original version (the gray and black part in Figure 2), the network structure is compressed based on time consideration; only the backbone network is retained. Therefore, the detection speed of YOLOv3-Tiny is incredibly quick, and so it is very appropriate for situations with high real-time requirements. To reduce the loss of image information, there have been some studies on the problem of partial information loss caused by a convolution operation. The paper [48] adds context information to the convolutional network through the deconvolution layer and uses the context information to make the network learn features of different depths. GC-YOLOv3 [49] realizes trainable semantic fusion between the feature extraction network and FPN [35]. In order to reduce the loss of image data and obtain more semantic information in the image, [PERSON] et al. introduced the upsample layer into YOLOv3 [34] to obtain higher positioning and recognition accuracy of targets in an image than YOLOv2 [33]. Inspired by the methods of deconvolution and upsampling in the image processing, we tend to propose an economical and stronger VaryBlock module for this drawback, which might effectively make amends for some image data loss caused by downsampling. The improved YOLOv3-Tiny object detection algorithm described in this paper adds the VaryBlock module to the network structure. The problem of tiny object detection is actually because of the dearth of original image information, and also the same drawback exists in object detection of different scales. With the deepening of the network, several potential feature information or details usually disappear, which include a huge impact on the results of object detection. Additionally, downsampling and convolution operations can cause the loss of some image information, which is able to eventually have an effect on the detection results of targets of assorted sizes. Figure 1: Improved YOLOv3-Tiny object detection algorithm framework. Fusing the features of the collected image is a common methodology to cut back semantic loss in downsampling. Resnet [50], FPN [35], etc. use element-wise add fusion features. DenseNet (Densely Connected Convolutional Networks) [51] uses concatenate to fuse features. The add and concatenate techniques have their advantages and disadvantages in feature fusion.