## 3 Proposed Method
### Pseudocode of the Proposed Networks  
This section presents pseudocodes of the proposed networks. The proposed networks employ YOLOv3-SPP as the baseline model, thus the pseudocode of the proposed networks looks similar to that of the general convolutional neural network. Two pseudocodes are presented as 'Algorithm 1' and 'Algorithm 2'. In below, 'Algorithm 1' shows the pseudocode of the proposed training algorithm, and 'Algorithm 2' for the detection algorithm. ```
1:Inputs: The road view training set
2:Initialize:\(N\): number of epochs
3:Augment and resize the image
4:for\(i=1\) to \(N\)do
5: Forward-pass in YOLO-based model
6: Predict Bounding box \((x,y,w,h,conf)\) and class probabilities
7: Calculate Loss
8: Do Back-propagation
9: Update weight of the model
10: Calculate mean Average Precision
11:if\(m<\) mean Average Precision then
12: Save the model
13:\(m\) = mean Average Precision
14:endif
15:endfor
```  
**Algorithm 2** Detection Algorithm  
### Pseudocode of the Proposed Networks  
This section presents pseudocodes of the proposed networks. The proposed networks employ YOLOv3-SPP as the baseline model, thus the pseudocode of the proposed networks looks similar to that of the general convolutional neural network. Two pseudocodes are presented as 'Algorithm 1' and 'Algorithm 2'. In below, 'Algorithm 1' shows the pseudocode of the proposed training algorithm, and 'Algorithm 2' for the detection algorithm. ```
1:Inputs: The road view Test set
2:Initialize:\(N\): number of epochs
3: Resize the test image
4: Forward-pass in trained model
5: Predict Bounding box \(B\)\((x,y,w,h,conf)\) and class id for each anchor
6: Do Non-Maximum-Suppression
7:return Detection Result

Figure 10: Detailed architecture of SLCat. (‘\(*\)’ means the repetition of CBL). ## 4 Experiments

### Overview

In this section, we evaluate the effect of different projection approaches to the YOLOv3-SPP and different concatenated models from the fisheye and projected images. First, the evaluation metrics will be discussed briefly, then we will present the detection accuracy from four datasets: Synthetic Fisheye-CityScape, Synthetic Fisheye-KITTI, WoodScape, and Fisheye-Dongseongro. ### Experiments on Synthetic Fisheye-KITTI

#### 4.2.1 Implementation Details

We set 24 as the mini-batch size, 512 as the size of the image, 300 epochs, 0.00019 initial learning rates, and apply the cosine decay learning rate scheduling strategy. The momentum and weight decay are, respectively, set as 0.937 and 0.0005. We take 7 different categories into consideration for the evaluation: pedestrian, person sitting, cyclist, car, van, truck, and tram. Finally, we use total of 7481 images and evaluate the accuracy with 1498 images. #### 4.2.2 Comparison of the Projection

We compare our expandable spherical projection to fisheye and basic spherical images. Even though computational time is almost the same, our result effectively increases the information of the edge regions and reduces unnecessary pixels from the original fisheye image. Table 1 shows the result of the Average Precision (\(AP\)) from fisheye image and different spherical images. Except for the medium size of objects, our proposed projection achieves the best performance on overall detection accuracy, 4.4% higher in \(AP\) than the performance using the fisheye dataset. The detection results are presented in Figure 11.

#### 4.2.3 Comparison of the Concatenated Models

Table 2, Table 3 and Table 4 show the accuracy of three projection models when using three different feature-fused methods, SCat, LCat, and SLCat, respectively. For the feature-fused methods, SCat and SLCat show better \(AP\) than the baseline model, while LCat only presents higher \(AP_{L}\) on fisheye and spherical images. For the task of small object detection, SCat shows the positive effect, increasing the accuracy up to 0.9% at the fisheye image. In this KITTI dataset, \(AP_{S}\) are mostly higher in the fisheye images than the spherical-projected images.