## 2 Data and Methods
### You Look Only Once (YOLO) v3 Network Framework  
Yolo-v3 is grounded upon the custom CNN architecture, which is called DarkNet-53 [45]. The initial Yolo v1 architecture was inspired by GoogleNet, and it performs downsampling of the image and produces final predictions from a tensor. This tensor is obtained in a similar way as in the ROI pooling layer of the Faster R-CNN network. The next-generation Yolo v2 architecture uses a 30-layer architecture, which consists of 19 layers from Darknet-19 and an additional 11 layers adopted for object detection purposes. This new architecture provides more accurate and faster object detection results, but it often struggles with the detection of small objects in the region of interest. Moreover, it does not benefit from the advantages of the residual blocks or upsampling operations while Yolo v3 does.  
Yolo v3 consists of a fully convolutional architecture, which uses a variant of Darknet, which has 53 layers trained with the Imagenet classification dataset. For the object detection tasks, an additional 53 layers were added onto it, and the improved architecture trained with the Pascal VOC dataset. With this structural design, the Yolo v3 outperformed most of the detection algorithms, while it is still fast enough for the real-time applications. With the help of the residual connections and upsampling, the architecture can perform detections at three different scales from the specific layers of the structure [45]. This makes the architecture more efficient at the detection of smaller objects but results in slower processing than the previous versions due to the complexity of the framework (Figure 8).  
The shape of the detection kernel is \(1\times 1\times(\text{B}\times(5+\text{C}))\). In the v3 network, 3 pieces of an anchor are used for detection for each scale. Here, B is the number of the anchors on the feature map, 5 is for the 4 bounding box offsets, and one for object confidence. C is the number of categories. In the current research, the Yolo v3 network was used and the class was the only airplane, so the detection kernel shape was designed as \(1\times 1\times(3\times(5+1))\) for each scale. The first detection process was performed from the 82 nd layer, as the first 81 layers downsampled the input image by the size of 32 strides. If the input image has a size of 608 \(\times\) 608 pixels, that will be output as a feature map of \(18\times 18\) pixels in that layer. This corresponds to \(18\times 18\times 18\) detection features being obtained from this layer. After the first detection operation, the feature map was upsampled by a factor of 2. This upsampled feature map is was with the feature map arising from the 61 st layer. Then, a few \(1\times 1\) convolution operations were performed to fuse features and reduce the depth dimension. After that, the second detection is performed from the 94 th layer, which returns a detection feature map of \(36\times 36\times 18\). The same procedure was performed for the third scale at the 106 th layer, which yields a feature map of the \(72\times 72\times 18\) size. This means it produced 20,412 predicted boxes for each image. As in the SSD network, the final predictions were proposed after the N  
Figure 7: Detection results of occluded objects (**a**) Without the NMS algorithm, (**b**) With the NMS algorithm.